Overview

Dataset statistics

Number of variables54
Number of observations171
Missing cells2483
Missing cells (%)26.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory72.3 KiB
Average record size in memory432.7 B

Variable types

Numeric14
Text19
Categorical18
Unsupported2
DateTime1

Alerts

airdate has constant value ""Constant
id is highly overall correlated with _embedded.show.weight and 3 other fieldsHigh correlation
season is highly overall correlated with _embedded.show.id and 4 other fieldsHigh correlation
number is highly overall correlated with type and 8 other fieldsHigh correlation
runtime is highly overall correlated with _embedded.show.runtime and 10 other fieldsHigh correlation
rating.average is highly overall correlated with _embedded.show.rating.average and 13 other fieldsHigh correlation
_embedded.show.id is highly overall correlated with season and 7 other fieldsHigh correlation
_embedded.show.runtime is highly overall correlated with runtime and 11 other fieldsHigh correlation
_embedded.show.averageRuntime is highly overall correlated with runtime and 12 other fieldsHigh correlation
_embedded.show.rating.average is highly overall correlated with rating.average and 4 other fieldsHigh correlation
_embedded.show.weight is highly overall correlated with id and 7 other fieldsHigh correlation
_embedded.show.webChannel.id is highly overall correlated with _embedded.show.webChannel.country.name and 8 other fieldsHigh correlation
_embedded.show.externals.thetvdb is highly overall correlated with season and 10 other fieldsHigh correlation
_embedded.show.updated is highly overall correlated with _embedded.show.webChannel.officialSite and 5 other fieldsHigh correlation
_embedded.show.network.id is highly overall correlated with id and 16 other fieldsHigh correlation
type is highly overall correlated with number and 8 other fieldsHigh correlation
airtime is highly overall correlated with number and 11 other fieldsHigh correlation
airstamp is highly overall correlated with number and 15 other fieldsHigh correlation
_embedded.show.type is highly overall correlated with rating.average and 7 other fieldsHigh correlation
_embedded.show.language is highly overall correlated with rating.average and 12 other fieldsHigh correlation
_embedded.show.status is highly overall correlated with _embedded.show.network.id and 7 other fieldsHigh correlation
_embedded.show.ended is highly overall correlated with season and 22 other fieldsHigh correlation
_embedded.show.schedule.time is highly overall correlated with rating.average and 10 other fieldsHigh correlation
_embedded.show.webChannel.country.name is highly overall correlated with runtime and 16 other fieldsHigh correlation
_embedded.show.webChannel.country.code is highly overall correlated with runtime and 16 other fieldsHigh correlation
_embedded.show.webChannel.country.timezone is highly overall correlated with runtime and 16 other fieldsHigh correlation
_embedded.show.webChannel.officialSite is highly overall correlated with runtime and 24 other fieldsHigh correlation
_embedded.show.externals.tvrage is highly overall correlated with id and 16 other fieldsHigh correlation
_embedded.show.network.country.name is highly overall correlated with number and 23 other fieldsHigh correlation
_embedded.show.network.country.code is highly overall correlated with number and 23 other fieldsHigh correlation
_embedded.show.network.country.timezone is highly overall correlated with number and 23 other fieldsHigh correlation
_embedded.show.network.officialSite is highly overall correlated with id and 28 other fieldsHigh correlation
type is highly imbalanced (93.5%)Imbalance
airtime is highly imbalanced (61.6%)Imbalance
_embedded.show.schedule.time is highly imbalanced (64.1%)Imbalance
number has 2 (1.2%) missing valuesMissing
runtime has 11 (6.4%) missing valuesMissing
summary has 92 (53.8%) missing valuesMissing
rating.average has 139 (81.3%) missing valuesMissing
_embedded.show.language has 6 (3.5%) missing valuesMissing
_embedded.show.runtime has 109 (63.7%) missing valuesMissing
_embedded.show.averageRuntime has 12 (7.0%) missing valuesMissing
_embedded.show.ended has 106 (62.0%) missing valuesMissing
_embedded.show.officialSite has 17 (9.9%) missing valuesMissing
_embedded.show.rating.average has 128 (74.9%) missing valuesMissing
_embedded.show.webChannel.id has 4 (2.3%) missing valuesMissing
_embedded.show.webChannel.name has 4 (2.3%) missing valuesMissing
_embedded.show.webChannel.country.name has 67 (39.2%) missing valuesMissing
_embedded.show.webChannel.country.code has 67 (39.2%) missing valuesMissing
_embedded.show.webChannel.country.timezone has 67 (39.2%) missing valuesMissing
_embedded.show.webChannel.officialSite has 39 (22.8%) missing valuesMissing
_embedded.show.externals.tvrage has 169 (98.8%) missing valuesMissing
_embedded.show.externals.thetvdb has 53 (31.0%) missing valuesMissing
_embedded.show.externals.imdb has 88 (51.5%) missing valuesMissing
_embedded.show.image.medium has 8 (4.7%) missing valuesMissing
_embedded.show.image.original has 8 (4.7%) missing valuesMissing
_embedded.show.summary has 37 (21.6%) missing valuesMissing
image.medium has 91 (53.2%) missing valuesMissing
image.original has 91 (53.2%) missing valuesMissing
_embedded.show.network.id has 149 (87.1%) missing valuesMissing
_embedded.show.network.name has 149 (87.1%) missing valuesMissing
_embedded.show.network.country.name has 149 (87.1%) missing valuesMissing
_embedded.show.network.country.code has 149 (87.1%) missing valuesMissing
_embedded.show.network.country.timezone has 149 (87.1%) missing valuesMissing
_embedded.show.network.officialSite has 159 (93.0%) missing valuesMissing
_embedded.show._links.nextepisode.href has 164 (95.9%) missing valuesMissing
_embedded.show.externals.tvrage is uniformly distributedUniform
id has unique valuesUnique
url has unique valuesUnique
_links.self.href has unique valuesUnique
_embedded.show.genres is an unsupported type, check if it needs cleaning or further analysisUnsupported
_embedded.show.schedule.days is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-08-13 20:44:50.231446
Analysis finished2023-08-13 20:45:03.780173
Duration13.55 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct171
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2444653.7
Minimum2323161
Maximum2603381
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-08-13T15:45:03.836731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2323161
5-th percentile2400189
Q12427729
median2438318
Q32452719.5
95-th percentile2517978.5
Maximum2603381
Range280220
Interquartile range (IQR)24990.5

Descriptive statistics

Standard deviation38178.061
Coefficient of variation (CV)0.01561696
Kurtosis4.8623154
Mean2444653.7
Median Absolute Deviation (MAD)14399
Skewness1.3728024
Sum4.1803579 × 108
Variance1.4575643 × 109
MonotonicityNot monotonic
2023-08-13T15:45:03.919553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2450906 1
 
0.6%
2442731 1
 
0.6%
2454076 1
 
0.6%
2454077 1
 
0.6%
2442332 1
 
0.6%
2440940 1
 
0.6%
2442203 1
 
0.6%
2441097 1
 
0.6%
2489442 1
 
0.6%
2438066 1
 
0.6%
Other values (161) 161
94.2%
ValueCountFrequency (%)
2323161 1
0.6%
2327473 1
0.6%
2372672 1
0.6%
2372683 1
0.6%
2393535 1
0.6%
2395103 1
0.6%
2397824 1
0.6%
2399082 1
0.6%
2399220 1
0.6%
2401158 1
0.6%
ValueCountFrequency (%)
2603381 1
0.6%
2577817 1
0.6%
2573107 1
0.6%
2572524 1
0.6%
2568631 1
0.6%
2568630 1
0.6%
2550682 1
0.6%
2519147 1
0.6%
2517979 1
0.6%
2517978 1
0.6%

url
Text

UNIQUE 

Distinct171
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-08-13T15:45:04.234246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length146
Median length103
Mean length79.081871
Min length59

Characters and Unicode

Total characters13523
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique171 ?
Unique (%)100.0%

Sample

1st rowhttps://www.tvmaze.com/episodes/2450906/hocu-vse-znat-2x87-seria-87
2nd rowhttps://www.tvmaze.com/episodes/2450907/hocu-vse-znat-2x88-seria-88
3rd rowhttps://www.tvmaze.com/episodes/2438114/restoran-po-ponatiam-2x05-seria-15
4th rowhttps://www.tvmaze.com/episodes/2438115/restoran-po-ponatiam-2x06-seria-16
5th rowhttps://www.tvmaze.com/episodes/2413779/alisa-ne-mozet-zdat-1x08-8-seria
ValueCountFrequency (%)
https://www.tvmaze.com/episodes/2450906/hocu-vse-znat-2x87-seria-87 1
 
0.6%
https://www.tvmaze.com/episodes/2433630/zamerzsie-1x05-seria-5 1
 
0.6%
https://www.tvmaze.com/episodes/2476693/neizvestnoe-budusee-1x01-seria-1 1
 
0.6%
https://www.tvmaze.com/episodes/2438114/restoran-po-ponatiam-2x05-seria-15 1
 
0.6%
https://www.tvmaze.com/episodes/2438115/restoran-po-ponatiam-2x06-seria-16 1
 
0.6%
https://www.tvmaze.com/episodes/2413779/alisa-ne-mozet-zdat-1x08-8-seria 1
 
0.6%
https://www.tvmaze.com/episodes/2323161/zamerzsie-1x01-seria-1 1
 
0.6%
https://www.tvmaze.com/episodes/2433627/zamerzsie-1x02-seria-2 1
 
0.6%
https://www.tvmaze.com/episodes/2433628/zamerzsie-1x03-seria-3 1
 
0.6%
https://www.tvmaze.com/episodes/2433629/zamerzsie-1x04-seria-4 1
 
0.6%
Other values (161) 161
94.2%
2023-08-13T15:45:04.644337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1131
 
8.4%
- 1016
 
7.5%
s 872
 
6.4%
/ 855
 
6.3%
t 801
 
5.9%
o 720
 
5.3%
a 594
 
4.4%
w 579
 
4.3%
i 540
 
4.0%
p 499
 
3.7%
Other values (30) 5916
43.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9253
68.4%
Decimal Number 1886
 
13.9%
Other Punctuation 1368
 
10.1%
Dash Punctuation 1016
 
7.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1131
12.2%
s 872
 
9.4%
t 801
 
8.7%
o 720
 
7.8%
a 594
 
6.4%
w 579
 
6.3%
i 540
 
5.8%
p 499
 
5.4%
m 452
 
4.9%
d 362
 
3.9%
Other values (16) 2703
29.2%
Decimal Number
ValueCountFrequency (%)
2 361
19.1%
1 323
17.1%
4 266
14.1%
0 238
12.6%
3 201
10.7%
5 117
 
6.2%
8 105
 
5.6%
6 103
 
5.5%
7 87
 
4.6%
9 85
 
4.5%
Other Punctuation
ValueCountFrequency (%)
/ 855
62.5%
. 342
 
25.0%
: 171
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
- 1016
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9253
68.4%
Common 4270
31.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1131
12.2%
s 872
 
9.4%
t 801
 
8.7%
o 720
 
7.8%
a 594
 
6.4%
w 579
 
6.3%
i 540
 
5.8%
p 499
 
5.4%
m 452
 
4.9%
d 362
 
3.9%
Other values (16) 2703
29.2%
Common
ValueCountFrequency (%)
- 1016
23.8%
/ 855
20.0%
2 361
 
8.5%
. 342
 
8.0%
1 323
 
7.6%
4 266
 
6.2%
0 238
 
5.6%
3 201
 
4.7%
: 171
 
4.0%
5 117
 
2.7%
Other values (4) 380
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13523
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1131
 
8.4%
- 1016
 
7.5%
s 872
 
6.4%
/ 855
 
6.3%
t 801
 
5.9%
o 720
 
5.3%
a 594
 
4.4%
w 579
 
4.3%
i 540
 
4.0%
p 499
 
3.7%
Other values (30) 5916
43.7%

name
Text

Distinct149
Distinct (%)87.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-08-13T15:45:04.882382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length96
Median length52
Mean length15.871345
Min length3

Characters and Unicode

Total characters2714
Distinct characters144
Distinct categories14 ?
Distinct scripts4 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique136 ?
Unique (%)79.5%

Sample

1st rowСерия 87
2nd rowСерия 88
3rd rowСерия 15
4th rowСерия 16
5th row8 серия
ValueCountFrequency (%)
episode 31
 
6.0%
серия 26
 
5.0%
the 15
 
2.9%
1 12
 
2.3%
2 9
 
1.7%
a 8
 
1.5%
6 7
 
1.3%
odcinek 6
 
1.2%
3 6
 
1.2%
and 5
 
1.0%
Other values (339) 395
76.0%
2023-08-13T15:45:05.205807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
349
 
12.9%
e 214
 
7.9%
o 157
 
5.8%
i 120
 
4.4%
s 119
 
4.4%
a 109
 
4.0%
n 98
 
3.6%
t 90
 
3.3%
d 86
 
3.2%
r 73
 
2.7%
Other values (134) 1299
47.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1752
64.6%
Uppercase Letter 414
 
15.3%
Space Separator 349
 
12.9%
Decimal Number 122
 
4.5%
Other Punctuation 38
 
1.4%
Other Letter 30
 
1.1%
Dash Punctuation 2
 
0.1%
Math Symbol 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Other values (4) 4
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 214
 
12.2%
o 157
 
9.0%
i 120
 
6.8%
s 119
 
6.8%
a 109
 
6.2%
n 98
 
5.6%
t 90
 
5.1%
d 86
 
4.9%
r 73
 
4.2%
p 63
 
3.6%
Other values (51) 623
35.6%
Uppercase Letter
ValueCountFrequency (%)
E 55
 
13.3%
T 38
 
9.2%
С 30
 
7.2%
S 23
 
5.6%
A 21
 
5.1%
D 20
 
4.8%
P 18
 
4.3%
B 18
 
4.3%
O 18
 
4.3%
W 15
 
3.6%
Other values (34) 158
38.2%
Other Letter
ValueCountFrequency (%)
ا 7
23.3%
ل 7
23.3%
ة 3
10.0%
ح 2
 
6.7%
ق 2
 
6.7%
ي 2
 
6.7%
م 2
 
6.7%
ن 2
 
6.7%
ك 1
 
3.3%
ت 1
 
3.3%
Decimal Number
ValueCountFrequency (%)
1 31
25.4%
3 19
15.6%
2 18
14.8%
8 12
 
9.8%
4 11
 
9.0%
6 10
 
8.2%
5 9
 
7.4%
0 7
 
5.7%
7 3
 
2.5%
9 2
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 7
18.4%
' 7
18.4%
? 6
15.8%
, 5
13.2%
" 4
10.5%
: 4
10.5%
! 3
7.9%
¿ 1
 
2.6%
⁉ 1
 
2.6%
Space Separator
ValueCountFrequency (%)
349
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Math Symbol
ValueCountFrequency (%)
| 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Other Symbol
ValueCountFrequency (%)
№ 1
100.0%
Initial Punctuation
ValueCountFrequency (%)
« 1
100.0%
Final Punctuation
ValueCountFrequency (%)
» 1
100.0%
Currency Symbol
ValueCountFrequency (%)
€ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1842
67.9%
Common 518
 
19.1%
Cyrillic 324
 
11.9%
Arabic 30
 
1.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 214
 
11.6%
o 157
 
8.5%
i 120
 
6.5%
s 119
 
6.5%
a 109
 
5.9%
n 98
 
5.3%
t 90
 
4.9%
d 86
 
4.7%
r 73
 
4.0%
p 63
 
3.4%
Other values (51) 713
38.7%
Cyrillic
ValueCountFrequency (%)
и 44
13.6%
е 37
11.4%
р 34
 
10.5%
С 30
 
9.3%
я 27
 
8.3%
о 16
 
4.9%
а 16
 
4.9%
с 13
 
4.0%
т 10
 
3.1%
д 9
 
2.8%
Other values (34) 88
27.2%
Common
ValueCountFrequency (%)
349
67.4%
1 31
 
6.0%
3 19
 
3.7%
2 18
 
3.5%
8 12
 
2.3%
4 11
 
2.1%
6 10
 
1.9%
5 9
 
1.7%
. 7
 
1.4%
' 7
 
1.4%
Other values (18) 45
 
8.7%
Arabic
ValueCountFrequency (%)
ا 7
23.3%
ل 7
23.3%
ة 3
10.0%
ح 2
 
6.7%
ق 2
 
6.7%
ي 2
 
6.7%
م 2
 
6.7%
ن 2
 
6.7%
ك 1
 
3.3%
ت 1
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2341
86.3%
Cyrillic 324
 
11.9%
Arabic 30
 
1.1%
None 16
 
0.6%
Letterlike Symbols 1
 
< 0.1%
Punctuation 1
 
< 0.1%
Currency Symbols 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
349
 
14.9%
e 214
 
9.1%
o 157
 
6.7%
i 120
 
5.1%
s 119
 
5.1%
a 109
 
4.7%
n 98
 
4.2%
t 90
 
3.8%
d 86
 
3.7%
r 73
 
3.1%
Other values (64) 926
39.6%
Cyrillic
ValueCountFrequency (%)
и 44
13.6%
е 37
11.4%
р 34
 
10.5%
С 30
 
9.3%
я 27
 
8.3%
о 16
 
4.9%
а 16
 
4.9%
с 13
 
4.0%
т 10
 
3.1%
д 9
 
2.8%
Other values (34) 88
27.2%
Arabic
ValueCountFrequency (%)
ا 7
23.3%
ل 7
23.3%
ة 3
10.0%
ح 2
 
6.7%
ق 2
 
6.7%
ي 2
 
6.7%
م 2
 
6.7%
ن 2
 
6.7%
ك 1
 
3.3%
ت 1
 
3.3%
None
ValueCountFrequency (%)
ä 3
18.8%
ö 2
12.5%
á 2
12.5%
ñ 1
 
6.2%
í 1
 
6.2%
¿ 1
 
6.2%
ó 1
 
6.2%
ã 1
 
6.2%
ú 1
 
6.2%
Ä 1
 
6.2%
Other values (2) 2
12.5%
Letterlike Symbols
ValueCountFrequency (%)
№ 1
100.0%
Punctuation
ValueCountFrequency (%)
⁉ 1
100.0%
Currency Symbols
ValueCountFrequency (%)
€ 1
100.0%

season
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.017544
Minimum1
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-08-13T15:45:05.296660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile20.5
Maximum2022
Range2021
Interquartile range (IQR)2

Descriptive statistics

Standard deviation341.19662
Coefficient of variation (CV)5.5016146
Kurtosis30.124444
Mean62.017544
Median Absolute Deviation (MAD)0
Skewness5.6359974
Sum10605
Variance116415.13
MonotonicityNot monotonic
2023-08-13T15:45:05.365037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 111
64.9%
2 16
 
9.4%
3 11
 
6.4%
2022 5
 
2.9%
4 5
 
2.9%
5 4
 
2.3%
13 2
 
1.2%
6 2
 
1.2%
11 2
 
1.2%
9 2
 
1.2%
Other values (11) 11
 
6.4%
ValueCountFrequency (%)
1 111
64.9%
2 16
 
9.4%
3 11
 
6.4%
4 5
 
2.9%
5 4
 
2.3%
6 2
 
1.2%
7 1
 
0.6%
8 1
 
0.6%
9 2
 
1.2%
10 1
 
0.6%
ValueCountFrequency (%)
2022 5
2.9%
39 1
 
0.6%
28 1
 
0.6%
23 1
 
0.6%
22 1
 
0.6%
19 1
 
0.6%
17 1
 
0.6%
16 1
 
0.6%
13 2
 
1.2%
12 1
 
0.6%

number
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct45
Distinct (%)26.6%
Missing2
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean18.940828
Minimum1
Maximum265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-08-13T15:45:05.440206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q312
95-th percentile88.6
Maximum265
Range264
Interquartile range (IQR)9

Descriptive statistics

Standard deviation39.999361
Coefficient of variation (CV)2.1118063
Kurtosis19.074082
Mean18.940828
Median Absolute Deviation (MAD)4
Skewness4.1083447
Sum3201
Variance1599.9489
MonotonicityNot monotonic
2023-08-13T15:45:05.516643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
1 22
12.9%
6 16
 
9.4%
2 16
 
9.4%
3 16
 
9.4%
4 14
 
8.2%
5 12
 
7.0%
8 10
 
5.8%
11 6
 
3.5%
7 5
 
2.9%
12 5
 
2.9%
Other values (35) 47
27.5%
ValueCountFrequency (%)
1 22
12.9%
2 16
9.4%
3 16
9.4%
4 14
8.2%
5 12
7.0%
6 16
9.4%
7 5
 
2.9%
8 10
5.8%
9 1
 
0.6%
10 4
 
2.3%
ValueCountFrequency (%)
265 1
0.6%
240 1
0.6%
238 1
0.6%
152 1
0.6%
138 1
0.6%
116 1
0.6%
115 1
0.6%
96 1
0.6%
89 1
0.6%
88 1
0.6%

type
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
regular
169 
significant_special
 
1
insignificant_special
 
1

Length

Max length21
Median length7
Mean length7.1520468
Min length7

Characters and Unicode

Total characters1223
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.2%

Sample

1st rowregular
2nd rowregular
3rd rowregular
4th rowregular
5th rowregular

Common Values

ValueCountFrequency (%)
regular 169
98.8%
significant_special 1
 
0.6%
insignificant_special 1
 
0.6%

Length

2023-08-13T15:45:05.589369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-13T15:45:05.654986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
regular 169
98.8%
significant_special 1
 
0.6%
insignificant_special 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
r 338
27.6%
a 173
14.1%
e 171
14.0%
g 171
14.0%
l 171
14.0%
u 169
13.8%
i 9
 
0.7%
n 5
 
0.4%
s 4
 
0.3%
c 4
 
0.3%
Other values (4) 8
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1221
99.8%
Connector Punctuation 2
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 338
27.7%
a 173
14.2%
e 171
14.0%
g 171
14.0%
l 171
14.0%
u 169
13.8%
i 9
 
0.7%
n 5
 
0.4%
s 4
 
0.3%
c 4
 
0.3%
Other values (3) 6
 
0.5%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1221
99.8%
Common 2
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 338
27.7%
a 173
14.2%
e 171
14.0%
g 171
14.0%
l 171
14.0%
u 169
13.8%
i 9
 
0.7%
n 5
 
0.4%
s 4
 
0.3%
c 4
 
0.3%
Other values (3) 6
 
0.5%
Common
ValueCountFrequency (%)
_ 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1223
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 338
27.6%
a 173
14.1%
e 171
14.0%
g 171
14.0%
l 171
14.0%
u 169
13.8%
i 9
 
0.7%
n 5
 
0.4%
s 4
 
0.3%
c 4
 
0.3%
Other values (4) 8
 
0.7%

airdate
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2022-12-01
171 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1710
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-12-01
2nd row2022-12-01
3rd row2022-12-01
4th row2022-12-01
5th row2022-12-01

Common Values

ValueCountFrequency (%)
2022-12-01 171
100.0%

Length

2023-08-13T15:45:05.717644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-13T15:45:05.772459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-12-01 171
100.0%

Most occurring characters

ValueCountFrequency (%)
2 684
40.0%
0 342
20.0%
- 342
20.0%
1 342
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1368
80.0%
Dash Punctuation 342
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 684
50.0%
0 342
25.0%
1 342
25.0%
Dash Punctuation
ValueCountFrequency (%)
- 342
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1710
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 684
40.0%
0 342
20.0%
- 342
20.0%
1 342
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1710
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 684
40.0%
0 342
20.0%
- 342
20.0%
1 342
20.0%

airtime
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct19
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
131 
06:00
 
11
12:00
 
5
22:00
 
3
10:00
 
3
Other values (14)
18 

Length

Max length5
Median length0
Mean length1.1695906
Min length0

Characters and Unicode

Total characters200
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)6.4%

Sample

1st row
2nd row
3rd row
4th row
5th row12:00

Common Values

ValueCountFrequency (%)
131
76.6%
06:00 11
 
6.4%
12:00 5
 
2.9%
22:00 3
 
1.8%
10:00 3
 
1.8%
00:00 3
 
1.8%
17:35 2
 
1.2%
19:00 2
 
1.2%
20:30 1
 
0.6%
11:00 1
 
0.6%
Other values (9) 9
 
5.3%

Length

2023-08-13T15:45:05.829886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
06:00 11
27.5%
12:00 5
12.5%
22:00 3
 
7.5%
10:00 3
 
7.5%
00:00 3
 
7.5%
17:35 2
 
5.0%
19:00 2
 
5.0%
21:00 1
 
2.5%
19:30 1
 
2.5%
19:25 1
 
2.5%
Other values (8) 8
20.0%

Most occurring characters

ValueCountFrequency (%)
0 89
44.5%
: 40
20.0%
1 21
 
10.5%
2 16
 
8.0%
6 11
 
5.5%
5 7
 
3.5%
9 6
 
3.0%
3 5
 
2.5%
7 4
 
2.0%
8 1
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 160
80.0%
Other Punctuation 40
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 89
55.6%
1 21
 
13.1%
2 16
 
10.0%
6 11
 
6.9%
5 7
 
4.4%
9 6
 
3.8%
3 5
 
3.1%
7 4
 
2.5%
8 1
 
0.6%
Other Punctuation
ValueCountFrequency (%)
: 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 89
44.5%
: 40
20.0%
1 21
 
10.5%
2 16
 
8.0%
6 11
 
5.5%
5 7
 
3.5%
9 6
 
3.0%
3 5
 
2.5%
7 4
 
2.0%
8 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 89
44.5%
: 40
20.0%
1 21
 
10.5%
2 16
 
8.0%
6 11
 
5.5%
5 7
 
3.5%
9 6
 
3.0%
3 5
 
2.5%
7 4
 
2.0%
8 1
 
0.5%

airstamp
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2022-12-01T12:00:00+00:00
69 
2022-12-01T00:00:00+00:00
24 
2022-12-01T17:00:00+00:00
14 
2022-12-01T06:00:00+00:00
9 
2022-12-01T01:00:00+00:00
8 
Other values (21)
47 

Length

Max length25
Median length25
Mean length25
Min length25

Characters and Unicode

Total characters4275
Distinct characters13
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)7.6%

Sample

1st row2022-12-01T00:00:00+00:00
2nd row2022-12-01T00:00:00+00:00
3rd row2022-12-01T00:00:00+00:00
4th row2022-12-01T00:00:00+00:00
5th row2022-12-01T00:00:00+00:00

Common Values

ValueCountFrequency (%)
2022-12-01T12:00:00+00:00 69
40.4%
2022-12-01T00:00:00+00:00 24
 
14.0%
2022-12-01T17:00:00+00:00 14
 
8.2%
2022-12-01T06:00:00+00:00 9
 
5.3%
2022-12-01T01:00:00+00:00 8
 
4.7%
2022-12-01T11:00:00+00:00 8
 
4.7%
2022-12-01T04:00:00+00:00 7
 
4.1%
2022-12-01T03:00:00+00:00 5
 
2.9%
2022-12-01T16:00:00+00:00 4
 
2.3%
2022-12-01T02:00:00+00:00 3
 
1.8%
Other values (16) 20
 
11.7%

Length

2023-08-13T15:45:05.894703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2022-12-01t12:00:00+00:00 69
40.4%
2022-12-01t00:00:00+00:00 24
 
14.0%
2022-12-01t17:00:00+00:00 14
 
8.2%
2022-12-01t06:00:00+00:00 9
 
5.3%
2022-12-01t01:00:00+00:00 8
 
4.7%
2022-12-01t11:00:00+00:00 8
 
4.7%
2022-12-01t04:00:00+00:00 7
 
4.1%
2022-12-01t03:00:00+00:00 5
 
2.9%
2022-12-01t16:00:00+00:00 4
 
2.3%
2022-12-02t00:00:00+00:00 3
 
1.8%
Other values (16) 20
 
11.7%

Most occurring characters

ValueCountFrequency (%)
0 1793
41.9%
2 764
17.9%
: 513
 
12.0%
1 458
 
10.7%
- 342
 
8.0%
T 171
 
4.0%
+ 171
 
4.0%
7 15
 
0.4%
6 14
 
0.3%
3 13
 
0.3%
Other values (3) 21
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3078
72.0%
Other Punctuation 513
 
12.0%
Dash Punctuation 342
 
8.0%
Uppercase Letter 171
 
4.0%
Math Symbol 171
 
4.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1793
58.3%
2 764
24.8%
1 458
 
14.9%
7 15
 
0.5%
6 14
 
0.5%
3 13
 
0.4%
5 11
 
0.4%
4 7
 
0.2%
8 3
 
0.1%
Other Punctuation
ValueCountFrequency (%)
: 513
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 342
100.0%
Uppercase Letter
ValueCountFrequency (%)
T 171
100.0%
Math Symbol
ValueCountFrequency (%)
+ 171
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4104
96.0%
Latin 171
 
4.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1793
43.7%
2 764
18.6%
: 513
 
12.5%
1 458
 
11.2%
- 342
 
8.3%
+ 171
 
4.2%
7 15
 
0.4%
6 14
 
0.3%
3 13
 
0.3%
5 11
 
0.3%
Other values (2) 10
 
0.2%
Latin
ValueCountFrequency (%)
T 171
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4275
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1793
41.9%
2 764
17.9%
: 513
 
12.0%
1 458
 
10.7%
- 342
 
8.0%
T 171
 
4.0%
+ 171
 
4.0%
7 15
 
0.4%
6 14
 
0.3%
3 13
 
0.3%
Other values (3) 21
 
0.5%

runtime
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct52
Distinct (%)32.5%
Missing11
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean36.50625
Minimum2
Maximum240
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-08-13T15:45:05.966229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile10
Q123
median31
Q347
95-th percentile60
Maximum240
Range238
Interquartile range (IQR)24

Descriptive statistics

Standard deviation25.956664
Coefficient of variation (CV)0.71101972
Kurtosis28.746689
Mean36.50625
Median Absolute Deviation (MAD)13
Skewness4.2821136
Sum5841
Variance673.74839
MonotonicityNot monotonic
2023-08-13T15:45:06.045100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 12
 
7.0%
50 11
 
6.4%
30 10
 
5.8%
44 7
 
4.1%
49 7
 
4.1%
45 7
 
4.1%
22 7
 
4.1%
10 6
 
3.5%
15 5
 
2.9%
20 5
 
2.9%
Other values (42) 83
48.5%
(Missing) 11
 
6.4%
ValueCountFrequency (%)
2 2
 
1.2%
4 1
 
0.6%
5 1
 
0.6%
7 1
 
0.6%
10 6
3.5%
11 1
 
0.6%
12 3
1.8%
14 1
 
0.6%
15 5
2.9%
16 1
 
0.6%
ValueCountFrequency (%)
240 1
 
0.6%
180 1
 
0.6%
121 1
 
0.6%
73 1
 
0.6%
69 2
1.2%
60 4
2.3%
59 1
 
0.6%
58 3
1.8%
57 3
1.8%
54 1
 
0.6%

summary
Text

MISSING 

Distinct79
Distinct (%)100.0%
Missing92
Missing (%)53.8%
Memory size1.5 KiB
2023-08-13T15:45:06.216377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length512
Median length191
Mean length188.82278
Min length40

Characters and Unicode

Total characters14917
Distinct characters78
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique79 ?
Unique (%)100.0%

Sample

1st row<p>Gordon's day goes from bad to worse when, distracted by the impulsive actions of an attractive stranger, Ashley, he accidentally hits a stray dog with his car.</p>
2nd row<p>When Ashley makes a mess in Gordon's sock drawer, she makes an impromptu trip to Costco with Colin, Megan and her mother, to buy Gordon some new furniture.</p>
3rd row<p>Gordon accidentally sends Ashley a dick pic and chases her from a garage sale to a death bed to try and delete it.</p>
4th row<p>Ash and Gordon's awkward sexual encounter causes them to drift apart.</p>
5th row<p>The Echo Park gang are impressed when Ashley takes charge during a power outage.</p>
ValueCountFrequency (%)
the 106
 
4.3%
to 102
 
4.1%
a 86
 
3.5%
and 83
 
3.3%
in 63
 
2.5%
of 51
 
2.1%
her 36
 
1.4%
with 25
 
1.0%
she 22
 
0.9%
is 22
 
0.9%
Other values (1183) 1888
76.0%
2023-08-13T15:45:06.490723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2403
16.1%
e 1350
 
9.1%
a 972
 
6.5%
t 970
 
6.5%
n 849
 
5.7%
o 837
 
5.6%
s 830
 
5.6%
i 816
 
5.5%
r 706
 
4.7%
h 551
 
3.7%
Other values (68) 4633
31.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11230
75.3%
Space Separator 2405
 
16.1%
Uppercase Letter 480
 
3.2%
Other Punctuation 428
 
2.9%
Math Symbol 324
 
2.2%
Dash Punctuation 27
 
0.2%
Decimal Number 17
 
0.1%
Close Punctuation 2
 
< 0.1%
Initial Punctuation 2
 
< 0.1%
Open Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1350
12.0%
a 972
 
8.7%
t 970
 
8.6%
n 849
 
7.6%
o 837
 
7.5%
s 830
 
7.4%
i 816
 
7.3%
r 706
 
6.3%
h 551
 
4.9%
l 430
 
3.8%
Other values (17) 2919
26.0%
Uppercase Letter
ValueCountFrequency (%)
A 56
 
11.7%
T 47
 
9.8%
S 42
 
8.8%
L 39
 
8.1%
C 35
 
7.3%
B 24
 
5.0%
M 23
 
4.8%
W 23
 
4.8%
J 22
 
4.6%
G 22
 
4.6%
Other values (14) 147
30.6%
Other Punctuation
ValueCountFrequency (%)
. 159
37.1%
, 114
26.6%
/ 82
19.2%
' 55
 
12.9%
" 8
 
1.9%
! 4
 
0.9%
? 3
 
0.7%
; 1
 
0.2%
: 1
 
0.2%
… 1
 
0.2%
Decimal Number
ValueCountFrequency (%)
2 6
35.3%
6 2
 
11.8%
0 2
 
11.8%
9 2
 
11.8%
4 2
 
11.8%
1 2
 
11.8%
3 1
 
5.9%
Dash Punctuation
ValueCountFrequency (%)
- 21
77.8%
– 4
 
14.8%
— 2
 
7.4%
Space Separator
ValueCountFrequency (%)
2403
99.9%
  2
 
0.1%
Math Symbol
ValueCountFrequency (%)
< 162
50.0%
> 162
50.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Initial Punctuation
ValueCountFrequency (%)
‘ 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11710
78.5%
Common 3207
 
21.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1350
 
11.5%
a 972
 
8.3%
t 970
 
8.3%
n 849
 
7.3%
o 837
 
7.1%
s 830
 
7.1%
i 816
 
7.0%
r 706
 
6.0%
h 551
 
4.7%
l 430
 
3.7%
Other values (41) 3399
29.0%
Common
ValueCountFrequency (%)
2403
74.9%
< 162
 
5.1%
> 162
 
5.1%
. 159
 
5.0%
, 114
 
3.6%
/ 82
 
2.6%
' 55
 
1.7%
- 21
 
0.7%
" 8
 
0.2%
2 6
 
0.2%
Other values (17) 35
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14904
99.9%
Punctuation 9
 
0.1%
None 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2403
16.1%
e 1350
 
9.1%
a 972
 
6.5%
t 970
 
6.5%
n 849
 
5.7%
o 837
 
5.6%
s 830
 
5.6%
i 816
 
5.5%
r 706
 
4.7%
h 551
 
3.7%
Other values (62) 4620
31.0%
Punctuation
ValueCountFrequency (%)
– 4
44.4%
— 2
22.2%
‘ 2
22.2%
… 1
 
11.1%
None
ValueCountFrequency (%)
ñ 2
50.0%
  2
50.0%

rating.average
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)46.9%
Missing139
Missing (%)81.3%
Infinite0
Infinite (%)0.0%
Mean6.984375
Minimum3.3
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-08-13T15:45:06.576117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.3
5-th percentile6.2
Q16.575
median6.9
Q37.2
95-th percentile8.225
Maximum10
Range6.7
Interquartile range (IQR)0.625

Descriptive statistics

Standard deviation1.0314754
Coefficient of variation (CV)0.14768328
Kurtosis6.3009441
Mean6.984375
Median Absolute Deviation (MAD)0.3
Skewness-0.53410715
Sum223.5
Variance1.0639415
MonotonicityNot monotonic
2023-08-13T15:45:06.638195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
6.8 6
 
3.5%
7 5
 
2.9%
6.5 3
 
1.8%
6.2 3
 
1.8%
8 3
 
1.8%
7.1 2
 
1.2%
7.2 2
 
1.2%
7.7 1
 
0.6%
8.5 1
 
0.6%
10 1
 
0.6%
Other values (5) 5
 
2.9%
(Missing) 139
81.3%
ValueCountFrequency (%)
3.3 1
 
0.6%
6.2 3
1.8%
6.3 1
 
0.6%
6.5 3
1.8%
6.6 1
 
0.6%
6.7 1
 
0.6%
6.8 6
3.5%
7 5
2.9%
7.1 2
 
1.2%
7.2 2
 
1.2%
ValueCountFrequency (%)
10 1
 
0.6%
8.5 1
 
0.6%
8 3
1.8%
7.9 1
 
0.6%
7.7 1
 
0.6%
7.2 2
 
1.2%
7.1 2
 
1.2%
7 5
2.9%
6.8 6
3.5%
6.7 1
 
0.6%

_links.self.href
Text

UNIQUE 

Distinct171
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-08-13T15:45:06.850459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length39
Mean length39
Min length39

Characters and Unicode

Total characters6669
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique171 ?
Unique (%)100.0%

Sample

1st rowhttps://api.tvmaze.com/episodes/2450906
2nd rowhttps://api.tvmaze.com/episodes/2450907
3rd rowhttps://api.tvmaze.com/episodes/2438114
4th rowhttps://api.tvmaze.com/episodes/2438115
5th rowhttps://api.tvmaze.com/episodes/2413779
ValueCountFrequency (%)
https://api.tvmaze.com/episodes/2450906 1
 
0.6%
https://api.tvmaze.com/episodes/2433630 1
 
0.6%
https://api.tvmaze.com/episodes/2476693 1
 
0.6%
https://api.tvmaze.com/episodes/2438114 1
 
0.6%
https://api.tvmaze.com/episodes/2438115 1
 
0.6%
https://api.tvmaze.com/episodes/2413779 1
 
0.6%
https://api.tvmaze.com/episodes/2323161 1
 
0.6%
https://api.tvmaze.com/episodes/2433627 1
 
0.6%
https://api.tvmaze.com/episodes/2433628 1
 
0.6%
https://api.tvmaze.com/episodes/2433629 1
 
0.6%
Other values (161) 161
94.2%
2023-08-13T15:45:07.151724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 684
 
10.3%
p 513
 
7.7%
s 513
 
7.7%
e 513
 
7.7%
t 513
 
7.7%
o 342
 
5.1%
a 342
 
5.1%
i 342
 
5.1%
. 342
 
5.1%
m 342
 
5.1%
Other values (16) 2223
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4275
64.1%
Other Punctuation 1197
 
17.9%
Decimal Number 1197
 
17.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p 513
12.0%
s 513
12.0%
e 513
12.0%
t 513
12.0%
o 342
8.0%
a 342
8.0%
i 342
8.0%
m 342
8.0%
h 171
 
4.0%
d 171
 
4.0%
Other values (3) 513
12.0%
Decimal Number
ValueCountFrequency (%)
2 271
22.6%
4 223
18.6%
3 134
11.2%
0 98
 
8.2%
1 97
 
8.1%
5 84
 
7.0%
7 76
 
6.3%
9 74
 
6.2%
6 71
 
5.9%
8 69
 
5.8%
Other Punctuation
ValueCountFrequency (%)
/ 684
57.1%
. 342
28.6%
: 171
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 4275
64.1%
Common 2394
35.9%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 684
28.6%
. 342
14.3%
2 271
 
11.3%
4 223
 
9.3%
: 171
 
7.1%
3 134
 
5.6%
0 98
 
4.1%
1 97
 
4.1%
5 84
 
3.5%
7 76
 
3.2%
Other values (3) 214
 
8.9%
Latin
ValueCountFrequency (%)
p 513
12.0%
s 513
12.0%
e 513
12.0%
t 513
12.0%
o 342
8.0%
a 342
8.0%
i 342
8.0%
m 342
8.0%
h 171
 
4.0%
d 171
 
4.0%
Other values (3) 513
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6669
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 684
 
10.3%
p 513
 
7.7%
s 513
 
7.7%
e 513
 
7.7%
t 513
 
7.7%
o 342
 
5.1%
a 342
 
5.1%
i 342
 
5.1%
. 342
 
5.1%
m 342
 
5.1%
Other values (16) 2223
33.3%

_links.show.href
Text

Distinct110
Distinct (%)64.3%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-08-13T15:45:07.378516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length34
Mean length33.964912
Min length31

Characters and Unicode

Total characters5808
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83 ?
Unique (%)48.5%

Sample

1st rowhttps://api.tvmaze.com/shows/55724
2nd rowhttps://api.tvmaze.com/shows/55724
3rd rowhttps://api.tvmaze.com/shows/59201
4th rowhttps://api.tvmaze.com/shows/59201
5th rowhttps://api.tvmaze.com/shows/60687
ValueCountFrequency (%)
https://api.tvmaze.com/shows/64860 8
 
4.7%
https://api.tvmaze.com/shows/61880 7
 
4.1%
https://api.tvmaze.com/shows/65188 6
 
3.5%
https://api.tvmaze.com/shows/65365 6
 
3.5%
https://api.tvmaze.com/shows/60736 6
 
3.5%
https://api.tvmaze.com/shows/66150 5
 
2.9%
https://api.tvmaze.com/shows/66583 5
 
2.9%
https://api.tvmaze.com/shows/65911 4
 
2.3%
https://api.tvmaze.com/shows/67168 3
 
1.8%
https://api.tvmaze.com/shows/47633 3
 
1.8%
Other values (100) 118
69.0%
2023-08-13T15:45:07.673084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 684
 
11.8%
s 513
 
8.8%
t 513
 
8.8%
h 342
 
5.9%
p 342
 
5.9%
a 342
 
5.9%
. 342
 
5.9%
o 342
 
5.9%
m 342
 
5.9%
6 202
 
3.5%
Other values (16) 1844
31.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3762
64.8%
Other Punctuation 1197
 
20.6%
Decimal Number 849
 
14.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 513
13.6%
t 513
13.6%
h 342
9.1%
p 342
9.1%
a 342
9.1%
o 342
9.1%
m 342
9.1%
e 171
 
4.5%
w 171
 
4.5%
c 171
 
4.5%
Other values (3) 513
13.6%
Decimal Number
ValueCountFrequency (%)
6 202
23.8%
5 101
11.9%
4 95
11.2%
1 87
10.2%
8 82
9.7%
3 69
 
8.1%
0 62
 
7.3%
2 62
 
7.3%
7 47
 
5.5%
9 42
 
4.9%
Other Punctuation
ValueCountFrequency (%)
/ 684
57.1%
. 342
28.6%
: 171
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 3762
64.8%
Common 2046
35.2%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 684
33.4%
. 342
16.7%
6 202
 
9.9%
: 171
 
8.4%
5 101
 
4.9%
4 95
 
4.6%
1 87
 
4.3%
8 82
 
4.0%
3 69
 
3.4%
0 62
 
3.0%
Other values (3) 151
 
7.4%
Latin
ValueCountFrequency (%)
s 513
13.6%
t 513
13.6%
h 342
9.1%
p 342
9.1%
a 342
9.1%
o 342
9.1%
m 342
9.1%
e 171
 
4.5%
w 171
 
4.5%
c 171
 
4.5%
Other values (3) 513
13.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 684
 
11.8%
s 513
 
8.8%
t 513
 
8.8%
h 342
 
5.9%
p 342
 
5.9%
a 342
 
5.9%
. 342
 
5.9%
o 342
 
5.9%
m 342
 
5.9%
6 202
 
3.5%
Other values (16) 1844
31.7%

_embedded.show.id
Real number (ℝ)

HIGH CORRELATION 

Distinct110
Distinct (%)64.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56436.889
Minimum81
Maximum69351
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-08-13T15:45:07.767525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum81
5-th percentile19670.5
Q154367.5
median63480
Q365365
95-th percentile67431
Maximum69351
Range69270
Interquartile range (IQR)10997.5

Descriptive statistics

Standard deviation15217.481
Coefficient of variation (CV)0.26963713
Kurtosis3.0606066
Mean56436.889
Median Absolute Deviation (MAD)2670
Skewness-1.9347318
Sum9650708
Variance2.3157173 × 108
MonotonicityNot monotonic
2023-08-13T15:45:07.848310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64860 8
 
4.7%
61880 7
 
4.1%
65188 6
 
3.5%
65365 6
 
3.5%
60736 6
 
3.5%
66150 5
 
2.9%
66583 5
 
2.9%
65911 4
 
2.3%
62166 3
 
1.8%
47633 3
 
1.8%
Other values (100) 118
69.0%
ValueCountFrequency (%)
81 1
0.6%
4175 1
0.6%
6220 1
0.6%
6441 1
0.6%
13215 1
0.6%
13818 2
1.2%
16753 1
0.6%
17046 1
0.6%
22295 1
0.6%
24727 1
0.6%
ValueCountFrequency (%)
69351 1
 
0.6%
69238 2
 
1.2%
68784 1
 
0.6%
68144 1
 
0.6%
67907 1
 
0.6%
67883 2
 
1.2%
67694 1
 
0.6%
67168 3
1.8%
67082 1
 
0.6%
66583 5
2.9%

_embedded.show.url
Text

Distinct110
Distinct (%)64.3%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-08-13T15:45:08.074795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length83
Median length65
Mean length52.099415
Min length39

Characters and Unicode

Total characters8909
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83 ?
Unique (%)48.5%

Sample

1st rowhttps://www.tvmaze.com/shows/55724/hocu-vse-znat
2nd rowhttps://www.tvmaze.com/shows/55724/hocu-vse-znat
3rd rowhttps://www.tvmaze.com/shows/59201/restoran-po-ponatiam
4th rowhttps://www.tvmaze.com/shows/59201/restoran-po-ponatiam
5th rowhttps://www.tvmaze.com/shows/60687/alisa-ne-mozet-zdat
ValueCountFrequency (%)
https://www.tvmaze.com/shows/64860/colin-from-accounts 8
 
4.7%
https://www.tvmaze.com/shows/61880/zamerzsie 7
 
4.1%
https://www.tvmaze.com/shows/65188/planet-sex-with-cara-delevingne 6
 
3.5%
https://www.tvmaze.com/shows/65365/pewnego-razu-na-krajowej-jedynce 6
 
3.5%
https://www.tvmaze.com/shows/60736/the-flatshare 6
 
3.5%
https://www.tvmaze.com/shows/66150/facil 5
 
2.9%
https://www.tvmaze.com/shows/66583/neizvestnoe-budusee 5
 
2.9%
https://www.tvmaze.com/shows/65911/toxisch 4
 
2.3%
https://www.tvmaze.com/shows/67168/cocaine-prison-likes-la-vraie-histoire-disabelle 3
 
1.8%
https://www.tvmaze.com/shows/47633/tyler-perrys-bruh 3
 
1.8%
Other values (100) 118
69.0%
2023-08-13T15:45:08.468422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 855
 
9.6%
w 726
 
8.1%
s 681
 
7.6%
t 658
 
7.4%
o 525
 
5.9%
e 479
 
5.4%
h 439
 
4.9%
m 408
 
4.6%
a 401
 
4.5%
. 342
 
3.8%
Other values (30) 3395
38.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6364
71.4%
Other Punctuation 1368
 
15.4%
Decimal Number 860
 
9.7%
Dash Punctuation 317
 
3.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 726
11.4%
s 681
10.7%
t 658
10.3%
o 525
 
8.2%
e 479
 
7.5%
h 439
 
6.9%
m 408
 
6.4%
a 401
 
6.3%
c 262
 
4.1%
p 239
 
3.8%
Other values (16) 1546
24.3%
Decimal Number
ValueCountFrequency (%)
6 203
23.6%
5 101
11.7%
4 97
11.3%
1 89
10.3%
8 83
9.7%
3 69
 
8.0%
2 65
 
7.6%
0 64
 
7.4%
7 47
 
5.5%
9 42
 
4.9%
Other Punctuation
ValueCountFrequency (%)
/ 855
62.5%
. 342
 
25.0%
: 171
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
- 317
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6364
71.4%
Common 2545
 
28.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 726
11.4%
s 681
10.7%
t 658
10.3%
o 525
 
8.2%
e 479
 
7.5%
h 439
 
6.9%
m 408
 
6.4%
a 401
 
6.3%
c 262
 
4.1%
p 239
 
3.8%
Other values (16) 1546
24.3%
Common
ValueCountFrequency (%)
/ 855
33.6%
. 342
 
13.4%
- 317
 
12.5%
6 203
 
8.0%
: 171
 
6.7%
5 101
 
4.0%
4 97
 
3.8%
1 89
 
3.5%
8 83
 
3.3%
3 69
 
2.7%
Other values (4) 218
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8909
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 855
 
9.6%
w 726
 
8.1%
s 681
 
7.6%
t 658
 
7.4%
o 525
 
5.9%
e 479
 
5.4%
h 439
 
4.9%
m 408
 
4.6%
a 401
 
4.5%
. 342
 
3.8%
Other values (30) 3395
38.1%

_embedded.show.name
Text

Distinct110
Distinct (%)64.3%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-08-13T15:45:08.676597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length53
Median length30
Mean length17.385965
Min length4

Characters and Unicode

Total characters2973
Distinct characters113
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83 ?
Unique (%)48.5%

Sample

1st rowХочу все знать!
2nd rowХочу все знать!
3rd rowРесторан по понятиям
4th rowРесторан по понятиям
5th rowАлиса не может ждать
ValueCountFrequency (%)
the 15
 
3.1%
of 9
 
1.8%
colin 8
 
1.6%
sex 8
 
1.6%
from 8
 
1.6%
accounts 8
 
1.6%
with 7
 
1.4%
замерзшие 7
 
1.4%
planet 6
 
1.2%
cara 6
 
1.2%
Other values (274) 408
83.3%
2023-08-13T15:45:08.973765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
319
 
10.7%
e 237
 
8.0%
a 166
 
5.6%
i 150
 
5.0%
n 150
 
5.0%
o 141
 
4.7%
r 135
 
4.5%
l 112
 
3.8%
s 105
 
3.5%
t 91
 
3.1%
Other values (103) 1367
46.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2189
73.6%
Uppercase Letter 416
 
14.0%
Space Separator 319
 
10.7%
Other Punctuation 36
 
1.2%
Decimal Number 11
 
0.4%
Dash Punctuation 2
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 237
 
10.8%
a 166
 
7.6%
i 150
 
6.9%
n 150
 
6.9%
o 141
 
6.4%
r 135
 
6.2%
l 112
 
5.1%
s 105
 
4.8%
t 91
 
4.2%
h 72
 
3.3%
Other values (50) 830
37.9%
Uppercase Letter
ValueCountFrequency (%)
T 33
 
7.9%
C 33
 
7.9%
P 32
 
7.7%
F 28
 
6.7%
S 28
 
6.7%
D 24
 
5.8%
A 23
 
5.5%
H 22
 
5.3%
M 19
 
4.6%
L 17
 
4.1%
Other values (28) 157
37.7%
Other Punctuation
ValueCountFrequency (%)
' 12
33.3%
: 7
19.4%
! 5
13.9%
, 5
13.9%
& 4
 
11.1%
. 2
 
5.6%
# 1
 
2.8%
Decimal Number
ValueCountFrequency (%)
2 3
27.3%
4 2
18.2%
0 2
18.2%
1 2
18.2%
8 1
 
9.1%
6 1
 
9.1%
Space Separator
ValueCountFrequency (%)
319
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2199
74.0%
Cyrillic 406
 
13.7%
Common 368
 
12.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 237
 
10.8%
a 166
 
7.5%
i 150
 
6.8%
n 150
 
6.8%
o 141
 
6.4%
r 135
 
6.1%
l 112
 
5.1%
s 105
 
4.8%
t 91
 
4.1%
h 72
 
3.3%
Other values (48) 840
38.2%
Cyrillic
ValueCountFrequency (%)
е 59
 
14.5%
о 33
 
8.1%
и 26
 
6.4%
н 24
 
5.9%
а 22
 
5.4%
у 20
 
4.9%
т 20
 
4.9%
с 19
 
4.7%
р 19
 
4.7%
з 16
 
3.9%
Other values (30) 148
36.5%
Common
ValueCountFrequency (%)
319
86.7%
' 12
 
3.3%
: 7
 
1.9%
! 5
 
1.4%
, 5
 
1.4%
& 4
 
1.1%
2 3
 
0.8%
4 2
 
0.5%
0 2
 
0.5%
. 2
 
0.5%
Other values (5) 7
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2552
85.8%
Cyrillic 406
 
13.7%
None 15
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
319
 
12.5%
e 237
 
9.3%
a 166
 
6.5%
i 150
 
5.9%
n 150
 
5.9%
o 141
 
5.5%
r 135
 
5.3%
l 112
 
4.4%
s 105
 
4.1%
t 91
 
3.6%
Other values (56) 946
37.1%
Cyrillic
ValueCountFrequency (%)
е 59
 
14.5%
о 33
 
8.1%
и 26
 
6.4%
н 24
 
5.9%
а 22
 
5.4%
у 20
 
4.9%
т 20
 
4.9%
с 19
 
4.7%
р 19
 
4.7%
з 16
 
3.9%
Other values (30) 148
36.5%
None
ValueCountFrequency (%)
á 5
33.3%
ï 3
20.0%
é 3
20.0%
ä 1
 
6.7%
ö 1
 
6.7%
ó 1
 
6.7%
å 1
 
6.7%

_embedded.show.type
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Scripted
97 
Documentary
20 
Animation
18 
Reality
17 
Talk Show
 
9
Other values (4)
10 

Length

Max length11
Median length8
Mean length8.3157895
Min length4

Characters and Unicode

Total characters1422
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st rowDocumentary
2nd rowDocumentary
3rd rowScripted
4th rowScripted
5th rowScripted

Common Values

ValueCountFrequency (%)
Scripted 97
56.7%
Documentary 20
 
11.7%
Animation 18
 
10.5%
Reality 17
 
9.9%
Talk Show 9
 
5.3%
Game Show 3
 
1.8%
News 3
 
1.8%
Sports 3
 
1.8%
Variety 1
 
0.6%

Length

2023-08-13T15:45:09.070772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-13T15:45:09.141607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
scripted 97
53.0%
documentary 20
 
10.9%
animation 18
 
9.8%
reality 17
 
9.3%
show 12
 
6.6%
talk 9
 
4.9%
game 3
 
1.6%
news 3
 
1.6%
sports 3
 
1.6%
variety 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
t 156
11.0%
i 151
10.6%
e 141
9.9%
r 121
 
8.5%
c 117
 
8.2%
S 112
 
7.9%
p 100
 
7.0%
d 97
 
6.8%
a 68
 
4.8%
n 56
 
3.9%
Other values (17) 303
21.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1227
86.3%
Uppercase Letter 183
 
12.9%
Space Separator 12
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 156
12.7%
i 151
12.3%
e 141
11.5%
r 121
9.9%
c 117
9.5%
p 100
8.1%
d 97
7.9%
a 68
5.5%
n 56
 
4.6%
o 53
 
4.3%
Other values (8) 167
13.6%
Uppercase Letter
ValueCountFrequency (%)
S 112
61.2%
D 20
 
10.9%
A 18
 
9.8%
R 17
 
9.3%
T 9
 
4.9%
G 3
 
1.6%
N 3
 
1.6%
V 1
 
0.5%
Space Separator
ValueCountFrequency (%)
12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1410
99.2%
Common 12
 
0.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 156
11.1%
i 151
10.7%
e 141
10.0%
r 121
8.6%
c 117
8.3%
S 112
 
7.9%
p 100
 
7.1%
d 97
 
6.9%
a 68
 
4.8%
n 56
 
4.0%
Other values (16) 291
20.6%
Common
ValueCountFrequency (%)
12
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1422
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 156
11.0%
i 151
10.6%
e 141
9.9%
r 121
 
8.5%
c 117
 
8.2%
S 112
 
7.9%
p 100
 
7.0%
d 97
 
6.8%
a 68
 
4.8%
n 56
 
3.9%
Other values (17) 303
21.3%

_embedded.show.language
Categorical

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)10.9%
Missing6
Missing (%)3.5%
Memory size1.5 KiB
English
64 
Russian
28 
Chinese
17 
Korean
9 
Spanish
7 
Other values (13)
40 

Length

Max length10
Median length7
Mean length6.9454545
Min length4

Characters and Unicode

Total characters1146
Distinct characters35
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)1.8%

Sample

1st rowRussian
2nd rowRussian
3rd rowRussian
4th rowRussian
5th rowRussian

Common Values

ValueCountFrequency (%)
English 64
37.4%
Russian 28
16.4%
Chinese 17
 
9.9%
Korean 9
 
5.3%
Spanish 7
 
4.1%
Polish 6
 
3.5%
Norwegian 5
 
2.9%
German 5
 
2.9%
French 4
 
2.3%
Japanese 4
 
2.3%
Other values (8) 16
 
9.4%
(Missing) 6
 
3.5%

Length

2023-08-13T15:45:09.223169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
english 64
38.8%
russian 28
17.0%
chinese 17
 
10.3%
korean 9
 
5.5%
spanish 7
 
4.2%
polish 6
 
3.6%
norwegian 5
 
3.0%
german 5
 
3.0%
arabic 4
 
2.4%
japanese 4
 
2.4%
Other values (8) 16
 
9.7%

Most occurring characters

ValueCountFrequency (%)
s 159
13.9%
n 150
13.1%
i 140
12.2%
h 104
9.1%
e 74
 
6.5%
a 73
 
6.4%
l 73
 
6.4%
g 71
 
6.2%
E 64
 
5.6%
u 34
 
3.0%
Other values (25) 204
17.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 981
85.6%
Uppercase Letter 165
 
14.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 159
16.2%
n 150
15.3%
i 140
14.3%
h 104
10.6%
e 74
7.5%
a 73
7.4%
l 73
7.4%
g 71
7.2%
u 34
 
3.5%
r 30
 
3.1%
Other values (10) 73
7.4%
Uppercase Letter
ValueCountFrequency (%)
E 64
38.8%
R 28
17.0%
C 17
 
10.3%
S 12
 
7.3%
K 9
 
5.5%
P 8
 
4.8%
G 5
 
3.0%
N 5
 
3.0%
F 4
 
2.4%
J 4
 
2.4%
Other values (5) 9
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 1146
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 159
13.9%
n 150
13.1%
i 140
12.2%
h 104
9.1%
e 74
 
6.5%
a 73
 
6.4%
l 73
 
6.4%
g 71
 
6.2%
E 64
 
5.6%
u 34
 
3.0%
Other values (25) 204
17.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1146
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 159
13.9%
n 150
13.1%
i 140
12.2%
h 104
9.1%
e 74
 
6.5%
a 73
 
6.4%
l 73
 
6.4%
g 71
 
6.2%
E 64
 
5.6%
u 34
 
3.0%
Other values (25) 204
17.8%

_embedded.show.genres
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size1.5 KiB

_embedded.show.status
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Running
78 
Ended
65 
To Be Determined
28 

Length

Max length16
Median length7
Mean length7.7134503
Min length5

Characters and Unicode

Total characters1319
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRunning
2nd rowRunning
3rd rowTo Be Determined
4th rowTo Be Determined
5th rowEnded

Common Values

ValueCountFrequency (%)
Running 78
45.6%
Ended 65
38.0%
To Be Determined 28
 
16.4%

Length

2023-08-13T15:45:09.291897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-13T15:45:09.351201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
running 78
34.4%
ended 65
28.6%
to 28
 
12.3%
be 28
 
12.3%
determined 28
 
12.3%

Most occurring characters

ValueCountFrequency (%)
n 327
24.8%
e 177
13.4%
d 158
12.0%
i 106
 
8.0%
R 78
 
5.9%
u 78
 
5.9%
g 78
 
5.9%
E 65
 
4.9%
56
 
4.2%
T 28
 
2.1%
Other values (6) 168
12.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1036
78.5%
Uppercase Letter 227
 
17.2%
Space Separator 56
 
4.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 327
31.6%
e 177
17.1%
d 158
15.3%
i 106
 
10.2%
u 78
 
7.5%
g 78
 
7.5%
o 28
 
2.7%
t 28
 
2.7%
r 28
 
2.7%
m 28
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
R 78
34.4%
E 65
28.6%
T 28
 
12.3%
B 28
 
12.3%
D 28
 
12.3%
Space Separator
ValueCountFrequency (%)
56
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1263
95.8%
Common 56
 
4.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 327
25.9%
e 177
14.0%
d 158
12.5%
i 106
 
8.4%
R 78
 
6.2%
u 78
 
6.2%
g 78
 
6.2%
E 65
 
5.1%
T 28
 
2.2%
o 28
 
2.2%
Other values (5) 140
11.1%
Common
ValueCountFrequency (%)
56
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1319
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 327
24.8%
e 177
13.4%
d 158
12.0%
i 106
 
8.0%
R 78
 
5.9%
u 78
 
5.9%
g 78
 
5.9%
E 65
 
4.9%
56
 
4.2%
T 28
 
2.1%
Other values (6) 168
12.7%

_embedded.show.runtime
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)35.5%
Missing109
Missing (%)63.7%
Infinite0
Infinite (%)0.0%
Mean36.741935
Minimum2
Maximum240
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-08-13T15:45:09.411898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q114.25
median30
Q350
95-th percentile60
Maximum240
Range238
Interquartile range (IQR)35.75

Descriptive statistics

Standard deviation37.928723
Coefficient of variation (CV)1.0323006
Kurtosis15.674327
Mean36.741935
Median Absolute Deviation (MAD)18.5
Skewness3.4709548
Sum2278
Variance1438.588
MonotonicityNot monotonic
2023-08-13T15:45:09.478622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
50 10
 
5.8%
30 6
 
3.5%
20 6
 
3.5%
60 5
 
2.9%
10 5
 
2.9%
25 4
 
2.3%
12 3
 
1.8%
45 3
 
1.8%
15 3
 
1.8%
46 2
 
1.2%
Other values (12) 15
 
8.8%
(Missing) 109
63.7%
ValueCountFrequency (%)
2 2
 
1.2%
4 1
 
0.6%
5 2
 
1.2%
7 1
 
0.6%
10 5
2.9%
11 1
 
0.6%
12 3
1.8%
14 1
 
0.6%
15 3
1.8%
18 1
 
0.6%
ValueCountFrequency (%)
240 1
 
0.6%
180 1
 
0.6%
95 1
 
0.6%
60 5
2.9%
57 1
 
0.6%
50 10
5.8%
46 2
 
1.2%
45 3
 
1.8%
40 2
 
1.2%
30 6
3.5%

_embedded.show.averageRuntime
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct46
Distinct (%)28.9%
Missing12
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean34.930818
Minimum2
Maximum215
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-08-13T15:45:09.551057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile10
Q123
median30
Q346
95-th percentile60
Maximum215
Range213
Interquartile range (IQR)23

Descriptive statistics

Standard deviation24.487434
Coefficient of variation (CV)0.70102665
Kurtosis24.944243
Mean34.930818
Median Absolute Deviation (MAD)12
Skewness3.9417641
Sum5554
Variance599.63442
MonotonicityNot monotonic
2023-08-13T15:45:09.624671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
28 16
 
9.4%
23 14
 
8.2%
30 11
 
6.4%
49 9
 
5.3%
46 8
 
4.7%
34 8
 
4.7%
25 6
 
3.5%
15 6
 
3.5%
60 5
 
2.9%
10 5
 
2.9%
Other values (36) 71
41.5%
(Missing) 12
 
7.0%
ValueCountFrequency (%)
2 3
1.8%
4 1
 
0.6%
5 1
 
0.6%
7 1
 
0.6%
9 1
 
0.6%
10 5
2.9%
11 1
 
0.6%
12 3
1.8%
14 1
 
0.6%
15 6
3.5%
ValueCountFrequency (%)
215 1
 
0.6%
180 1
 
0.6%
98 1
 
0.6%
79 1
 
0.6%
65 1
 
0.6%
60 5
2.9%
59 1
 
0.6%
58 4
2.3%
57 3
1.8%
54 1
 
0.6%

_embedded.show.premiered
Date

Distinct74
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Minimum1995-10-23 00:00:00
Maximum2022-12-01 00:00:00
2023-08-13T15:45:09.699668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:09.775629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

_embedded.show.ended
Categorical

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)26.2%
Missing106
Missing (%)62.0%
Memory size1.5 KiB
2022-12-01
31 
2022-12-15
5 
2022-12-29
5 
2022-12-23
 
3
2022-12-03
 
3
Other values (12)
18 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters650
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)9.2%

Sample

1st row2022-12-01
2nd row2022-12-01
3rd row2022-12-01
4th row2022-12-01
5th row2022-12-01

Common Values

ValueCountFrequency (%)
2022-12-01 31
 
18.1%
2022-12-15 5
 
2.9%
2022-12-29 5
 
2.9%
2022-12-23 3
 
1.8%
2022-12-03 3
 
1.8%
2022-12-07 2
 
1.2%
2022-12-08 2
 
1.2%
2023-01-05 2
 
1.2%
2023-01-20 2
 
1.2%
2023-01-26 2
 
1.2%
Other values (7) 8
 
4.7%
(Missing) 106
62.0%

Length

2023-08-13T15:45:09.843403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2022-12-01 31
47.7%
2022-12-15 5
 
7.7%
2022-12-29 5
 
7.7%
2022-12-23 3
 
4.6%
2022-12-03 3
 
4.6%
2023-01-01 2
 
3.1%
2023-01-26 2
 
3.1%
2023-01-20 2
 
3.1%
2023-01-05 2
 
3.1%
2022-12-08 2
 
3.1%
Other values (7) 8
 
12.3%

Most occurring characters

ValueCountFrequency (%)
2 254
39.1%
- 130
20.0%
0 122
18.8%
1 103
15.8%
3 19
 
2.9%
5 8
 
1.2%
9 6
 
0.9%
7 2
 
0.3%
8 2
 
0.3%
6 2
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 520
80.0%
Dash Punctuation 130
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 254
48.8%
0 122
23.5%
1 103
19.8%
3 19
 
3.7%
5 8
 
1.5%
9 6
 
1.2%
7 2
 
0.4%
8 2
 
0.4%
6 2
 
0.4%
4 2
 
0.4%
Dash Punctuation
ValueCountFrequency (%)
- 130
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 650
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 254
39.1%
- 130
20.0%
0 122
18.8%
1 103
15.8%
3 19
 
2.9%
5 8
 
1.2%
9 6
 
0.9%
7 2
 
0.3%
8 2
 
0.3%
6 2
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 650
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 254
39.1%
- 130
20.0%
0 122
18.8%
1 103
15.8%
3 19
 
2.9%
5 8
 
1.2%
9 6
 
0.9%
7 2
 
0.3%
8 2
 
0.3%
6 2
 
0.3%

_embedded.show.officialSite
Text

MISSING 

Distinct98
Distinct (%)63.6%
Missing17
Missing (%)9.9%
Memory size1.5 KiB
2023-08-13T15:45:10.022041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length123
Median length67
Mean length47.811688
Min length17

Characters and Unicode

Total characters7363
Distinct characters75
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique73 ?
Unique (%)47.4%

Sample

1st rowhttps://hd.kinopoisk.ru/film/4615b1810bb9c3bdb9c439fd701162a4
2nd rowhttps://hd.kinopoisk.ru/film/4615b1810bb9c3bdb9c439fd701162a4
3rd rowhttps://premier.one/show/restoran-po-ponyatiyam
4th rowhttps://premier.one/show/restoran-po-ponyatiyam
5th rowhttps://start.ru/watch/alisa-ne-mozhet-zhdat
ValueCountFrequency (%)
https://binge.com.au 8
 
5.2%
https://www.ivi.ru/watch/zamerzshie 7
 
4.5%
https://www.netflix.com/title/81442123 6
 
3.9%
https://www.bbc.co.uk/programmes/p0df24z1 6
 
3.9%
https://www.paramountplus.com/shows/the-flatshare 6
 
3.9%
https://facil.movistarplus.es 5
 
3.2%
https://www.amazon.de/toxisch/dp/b0b8rpzgm5 4
 
2.6%
https://www.bet.plus/shows/tyler-perrys-bruh 3
 
1.9%
https://www.crave.ca/en/tv-shows/cocaine-prison-likes-isabelles-true-story 3
 
1.9%
https://www.bbc.co.uk/programmes/p0db975h 3
 
1.9%
Other values (88) 103
66.9%
2023-08-13T15:45:10.315751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 663
 
9.0%
t 532
 
7.2%
s 442
 
6.0%
e 367
 
5.0%
w 317
 
4.3%
o 311
 
4.2%
h 309
 
4.2%
. 304
 
4.1%
p 302
 
4.1%
a 295
 
4.0%
Other values (65) 3521
47.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5063
68.8%
Other Punctuation 1178
 
16.0%
Decimal Number 634
 
8.6%
Uppercase Letter 331
 
4.5%
Dash Punctuation 134
 
1.8%
Math Symbol 11
 
0.1%
Connector Punctuation 10
 
0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 532
 
10.5%
s 442
 
8.7%
e 367
 
7.2%
w 317
 
6.3%
o 311
 
6.1%
h 309
 
6.1%
p 302
 
6.0%
a 295
 
5.8%
i 248
 
4.9%
r 233
 
4.6%
Other values (16) 1707
33.7%
Uppercase Letter
ValueCountFrequency (%)
A 30
 
9.1%
P 24
 
7.3%
C 22
 
6.6%
E 21
 
6.3%
B 18
 
5.4%
S 17
 
5.1%
Z 15
 
4.5%
T 15
 
4.5%
G 15
 
4.5%
F 14
 
4.2%
Other values (16) 140
42.3%
Decimal Number
ValueCountFrequency (%)
0 101
15.9%
4 77
12.1%
2 74
11.7%
1 70
11.0%
3 59
9.3%
9 54
8.5%
8 54
8.5%
5 53
8.4%
6 51
8.0%
7 41
6.5%
Other Punctuation
ValueCountFrequency (%)
/ 663
56.3%
. 304
25.8%
: 175
 
14.9%
% 21
 
1.8%
? 10
 
0.8%
' 2
 
0.2%
& 2
 
0.2%
@ 1
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 134
100.0%
Math Symbol
ValueCountFrequency (%)
= 11
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5394
73.3%
Common 1969
 
26.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 532
 
9.9%
s 442
 
8.2%
e 367
 
6.8%
w 317
 
5.9%
o 311
 
5.8%
h 309
 
5.7%
p 302
 
5.6%
a 295
 
5.5%
i 248
 
4.6%
r 233
 
4.3%
Other values (42) 2038
37.8%
Common
ValueCountFrequency (%)
/ 663
33.7%
. 304
15.4%
: 175
 
8.9%
- 134
 
6.8%
0 101
 
5.1%
4 77
 
3.9%
2 74
 
3.8%
1 70
 
3.6%
3 59
 
3.0%
9 54
 
2.7%
Other values (13) 258
 
13.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7363
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 663
 
9.0%
t 532
 
7.2%
s 442
 
6.0%
e 367
 
5.0%
w 317
 
4.3%
o 311
 
4.2%
h 309
 
4.2%
. 304
 
4.1%
p 302
 
4.1%
a 295
 
4.0%
Other values (65) 3521
47.8%

_embedded.show.schedule.time
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct19
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
136 
12:00
 
6
00:00
 
4
10:00
 
3
08:30
 
3
Other values (14)
19 

Length

Max length5
Median length0
Mean length1.0233918
Min length0

Characters and Unicode

Total characters175
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)5.8%

Sample

1st row
2nd row
3rd row
4th row
5th row12:00

Common Values

ValueCountFrequency (%)
136
79.5%
12:00 6
 
3.5%
00:00 4
 
2.3%
10:00 3
 
1.8%
08:30 3
 
1.8%
22:00 3
 
1.8%
17:00 2
 
1.2%
17:35 2
 
1.2%
21:00 2
 
1.2%
20:00 1
 
0.6%
Other values (9) 9
 
5.3%

Length

2023-08-13T15:45:10.406021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
12:00 6
17.1%
00:00 4
11.4%
10:00 3
 
8.6%
08:30 3
 
8.6%
22:00 3
 
8.6%
17:00 2
 
5.7%
17:35 2
 
5.7%
21:00 2
 
5.7%
19:50 1
 
2.9%
19:25 1
 
2.9%
Other values (8) 8
22.9%

Most occurring characters

ValueCountFrequency (%)
0 73
41.7%
: 35
20.0%
1 22
 
12.6%
2 19
 
10.9%
3 7
 
4.0%
5 6
 
3.4%
7 5
 
2.9%
9 4
 
2.3%
8 3
 
1.7%
6 1
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 140
80.0%
Other Punctuation 35
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 73
52.1%
1 22
 
15.7%
2 19
 
13.6%
3 7
 
5.0%
5 6
 
4.3%
7 5
 
3.6%
9 4
 
2.9%
8 3
 
2.1%
6 1
 
0.7%
Other Punctuation
ValueCountFrequency (%)
: 35
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 175
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 73
41.7%
: 35
20.0%
1 22
 
12.6%
2 19
 
10.9%
3 7
 
4.0%
5 6
 
3.4%
7 5
 
2.9%
9 4
 
2.3%
8 3
 
1.7%
6 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 175
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 73
41.7%
: 35
20.0%
1 22
 
12.6%
2 19
 
10.9%
3 7
 
4.0%
5 6
 
3.4%
7 5
 
2.9%
9 4
 
2.3%
8 3
 
1.7%
6 1
 
0.6%

_embedded.show.schedule.days
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size1.5 KiB

_embedded.show.rating.average
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)34.9%
Missing128
Missing (%)74.9%
Infinite0
Infinite (%)0.0%
Mean6.7744186
Minimum1.1
Maximum8.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-08-13T15:45:10.464447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile4.58
Q15.8
median7.8
Q38
95-th percentile8.1
Maximum8.3
Range7.2
Interquartile range (IQR)2.2

Descriptive statistics

Standard deviation1.549515
Coefficient of variation (CV)0.22873033
Kurtosis3.2523044
Mean6.7744186
Median Absolute Deviation (MAD)0.5
Skewness-1.561165
Sum291.3
Variance2.4009967
MonotonicityNot monotonic
2023-08-13T15:45:10.528762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
8 10
 
5.8%
7.8 8
 
4.7%
5.8 5
 
2.9%
5.7 4
 
2.3%
8.1 2
 
1.2%
6 2
 
1.2%
6.7 2
 
1.2%
8.3 2
 
1.2%
5.4 2
 
1.2%
5.3 1
 
0.6%
Other values (5) 5
 
2.9%
(Missing) 128
74.9%
ValueCountFrequency (%)
1.1 1
 
0.6%
2.9 1
 
0.6%
4.5 1
 
0.6%
5.3 1
 
0.6%
5.4 2
 
1.2%
5.7 4
2.3%
5.8 5
2.9%
6 2
 
1.2%
6.7 2
 
1.2%
7 1
 
0.6%
ValueCountFrequency (%)
8.3 2
 
1.2%
8.1 2
 
1.2%
8 10
5.8%
7.8 8
4.7%
7.3 1
 
0.6%
7 1
 
0.6%
6.7 2
 
1.2%
6 2
 
1.2%
5.8 5
2.9%
5.7 4
 
2.3%

_embedded.show.weight
Real number (ℝ)

HIGH CORRELATION 

Distinct59
Distinct (%)34.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.292398
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-08-13T15:45:10.605002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q18
median32
Q376.5
95-th percentile95
Maximum99
Range98
Interquartile range (IQR)68.5

Descriptive statistics

Standard deviation34.318524
Coefficient of variation (CV)0.83110997
Kurtosis-1.4784625
Mean41.292398
Median Absolute Deviation (MAD)29
Skewness0.29154132
Sum7061
Variance1177.7611
MonotonicityNot monotonic
2023-08-13T15:45:10.683242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 13
 
7.6%
1 10
 
5.8%
19 9
 
5.3%
3 8
 
4.7%
2 8
 
4.7%
94 8
 
4.7%
8 6
 
3.5%
77 6
 
3.5%
67 6
 
3.5%
81 6
 
3.5%
Other values (49) 91
53.2%
ValueCountFrequency (%)
1 10
5.8%
2 8
4.7%
3 8
4.7%
4 13
7.6%
5 2
 
1.2%
8 6
3.5%
10 2
 
1.2%
11 5
 
2.9%
12 3
 
1.8%
13 2
 
1.2%
ValueCountFrequency (%)
99 3
 
1.8%
97 5
2.9%
95 2
 
1.2%
94 8
4.7%
92 1
 
0.6%
91 1
 
0.6%
87 3
 
1.8%
85 3
 
1.8%
83 1
 
0.6%
82 3
 
1.8%

_embedded.show.webChannel.id
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct56
Distinct (%)33.5%
Missing4
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean221.00599
Minimum1
Maximum552
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-08-13T15:45:10.755900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.7
Q159.5
median190
Q3359
95-th percentile529
Maximum552
Range551
Interquartile range (IQR)299.5

Descriptive statistics

Standard deviation173.5992
Coefficient of variation (CV)0.78549544
Kurtosis-1.1405429
Mean221.00599
Median Absolute Deviation (MAD)161
Skewness0.37357087
Sum36908
Variance30136.681
MonotonicityNot monotonic
2023-08-13T15:45:10.829807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 17
 
9.9%
107 10
 
5.8%
26 9
 
5.3%
522 8
 
4.7%
337 7
 
4.1%
366 6
 
3.5%
104 6
 
3.5%
1 6
 
3.5%
251 5
 
2.9%
281 5
 
2.9%
Other values (46) 88
51.5%
ValueCountFrequency (%)
1 6
 
3.5%
2 2
 
1.2%
3 1
 
0.6%
12 1
 
0.6%
15 1
 
0.6%
21 17
9.9%
26 9
5.3%
30 1
 
0.6%
51 3
 
1.8%
52 1
 
0.6%
ValueCountFrequency (%)
552 1
 
0.6%
546 1
 
0.6%
541 1
 
0.6%
540 1
 
0.6%
538 3
 
1.8%
532 2
 
1.2%
522 8
4.7%
490 2
 
1.2%
482 2
 
1.2%
467 2
 
1.2%

_embedded.show.webChannel.name
Text

MISSING 

Distinct56
Distinct (%)33.5%
Missing4
Missing (%)2.3%
Memory size1.5 KiB
2023-08-13T15:45:10.961851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length12
Mean length7.3473054
Min length3

Characters and Unicode

Total characters1227
Distinct characters68
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)15.0%

Sample

1st rowКиноПоиск HD
2nd rowКиноПоиск HD
3rd rowPremier
4th rowPremier
5th rowStart
ValueCountFrequency (%)
youtube 17
 
8.2%
paramount 10
 
4.8%
bbc 9
 
4.3%
iplayer 9
 
4.3%
binge 8
 
3.8%
ivi 7
 
3.4%
okko 6
 
2.9%
tencent 6
 
2.9%
qq 6
 
2.9%
netflix 6
 
2.9%
Other values (62) 124
59.6%
2023-08-13T15:45:11.188601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 120
 
9.8%
i 81
 
6.6%
a 73
 
5.9%
o 63
 
5.1%
u 61
 
5.0%
r 53
 
4.3%
n 49
 
4.0%
t 42
 
3.4%
T 42
 
3.4%
41
 
3.3%
Other values (58) 602
49.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 831
67.7%
Uppercase Letter 329
 
26.8%
Space Separator 41
 
3.3%
Math Symbol 23
 
1.9%
Other Punctuation 2
 
0.2%
Decimal Number 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 120
14.4%
i 81
 
9.7%
a 73
 
8.8%
o 63
 
7.6%
u 61
 
7.3%
r 53
 
6.4%
n 49
 
5.9%
t 42
 
5.1%
v 34
 
4.1%
l 33
 
4.0%
Other values (25) 222
26.7%
Uppercase Letter
ValueCountFrequency (%)
T 42
 
12.8%
B 37
 
11.2%
P 28
 
8.5%
Y 21
 
6.4%
C 18
 
5.5%
V 17
 
5.2%
Q 16
 
4.9%
N 14
 
4.3%
W 12
 
3.6%
O 12
 
3.6%
Other values (19) 112
34.0%
Space Separator
ValueCountFrequency (%)
41
100.0%
Math Symbol
ValueCountFrequency (%)
+ 23
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%
Decimal Number
ValueCountFrequency (%)
4 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1102
89.8%
Common 67
 
5.5%
Cyrillic 58
 
4.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 120
 
10.9%
i 81
 
7.4%
a 73
 
6.6%
o 63
 
5.7%
u 61
 
5.5%
r 53
 
4.8%
n 49
 
4.4%
t 42
 
3.8%
T 42
 
3.8%
B 37
 
3.4%
Other values (39) 481
43.6%
Cyrillic
ValueCountFrequency (%)
о 11
19.0%
и 11
19.0%
н 6
10.3%
с 6
10.3%
К 5
8.6%
П 5
8.6%
к 5
8.6%
д 2
 
3.4%
а 1
 
1.7%
В 1
 
1.7%
Other values (5) 5
8.6%
Common
ValueCountFrequency (%)
41
61.2%
+ 23
34.3%
. 2
 
3.0%
4 1
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1169
95.3%
Cyrillic 58
 
4.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 120
 
10.3%
i 81
 
6.9%
a 73
 
6.2%
o 63
 
5.4%
u 61
 
5.2%
r 53
 
4.5%
n 49
 
4.2%
t 42
 
3.6%
T 42
 
3.6%
41
 
3.5%
Other values (43) 544
46.5%
Cyrillic
ValueCountFrequency (%)
о 11
19.0%
и 11
19.0%
н 6
10.3%
с 6
10.3%
К 5
8.6%
П 5
8.6%
к 5
8.6%
д 2
 
3.4%
а 1
 
1.7%
В 1
 
1.7%
Other values (5) 5
8.6%

_embedded.show.webChannel.country.name
Categorical

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)14.4%
Missing67
Missing (%)39.2%
Memory size1.5 KiB
Russian Federation
30 
United States
16 
China
11 
United Kingdom
10 
Australia
8 
Other values (10)
29 

Length

Max length18
Median length14
Mean length12.365385
Min length5

Characters and Unicode

Total characters1286
Distinct characters34
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)3.8%

Sample

1st rowRussian Federation
2nd rowRussian Federation
3rd rowRussian Federation
4th rowRussian Federation
5th rowRussian Federation

Common Values

ValueCountFrequency (%)
Russian Federation 30
17.5%
United States 16
 
9.4%
China 11
 
6.4%
United Kingdom 10
 
5.8%
Australia 8
 
4.7%
Korea, Republic of 8
 
4.7%
Spain 5
 
2.9%
Canada 5
 
2.9%
Norway 3
 
1.8%
Japan 2
 
1.2%
Other values (5) 6
 
3.5%
(Missing) 67
39.2%

Length

2023-08-13T15:45:11.361021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
russian 30
17.0%
federation 30
17.0%
united 26
14.8%
states 16
9.1%
china 11
 
6.2%
kingdom 10
 
5.7%
republic 8
 
4.5%
of 8
 
4.5%
korea 8
 
4.5%
australia 8
 
4.5%
Other values (9) 21
11.9%

Most occurring characters

ValueCountFrequency (%)
a 141
11.0%
i 130
 
10.1%
e 127
 
9.9%
n 124
 
9.6%
t 97
 
7.5%
s 85
 
6.6%
d 74
 
5.8%
72
 
5.6%
o 59
 
4.6%
r 52
 
4.0%
Other values (24) 325
25.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1038
80.7%
Uppercase Letter 168
 
13.1%
Space Separator 72
 
5.6%
Other Punctuation 8
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 141
13.6%
i 130
12.5%
e 127
12.2%
n 124
11.9%
t 97
9.3%
s 85
8.2%
d 74
7.1%
o 59
5.7%
r 52
 
5.0%
u 47
 
4.5%
Other values (11) 102
9.8%
Uppercase Letter
ValueCountFrequency (%)
R 38
22.6%
F 30
17.9%
U 27
16.1%
S 23
13.7%
K 18
10.7%
C 16
9.5%
A 8
 
4.8%
N 4
 
2.4%
J 2
 
1.2%
B 1
 
0.6%
Space Separator
ValueCountFrequency (%)
72
100.0%
Other Punctuation
ValueCountFrequency (%)
, 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1206
93.8%
Common 80
 
6.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 141
11.7%
i 130
10.8%
e 127
10.5%
n 124
10.3%
t 97
 
8.0%
s 85
 
7.0%
d 74
 
6.1%
o 59
 
4.9%
r 52
 
4.3%
u 47
 
3.9%
Other values (22) 270
22.4%
Common
ValueCountFrequency (%)
72
90.0%
, 8
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1286
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 141
11.0%
i 130
 
10.1%
e 127
 
9.9%
n 124
 
9.6%
t 97
 
7.5%
s 85
 
6.6%
d 74
 
5.8%
72
 
5.6%
o 59
 
4.6%
r 52
 
4.0%
Other values (24) 325
25.3%

_embedded.show.webChannel.country.code
Categorical

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)14.4%
Missing67
Missing (%)39.2%
Memory size1.5 KiB
RU
30 
US
16 
CN
11 
GB
10 
AU
8 
Other values (10)
29 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters208
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)3.8%

Sample

1st rowRU
2nd rowRU
3rd rowRU
4th rowRU
5th rowRU

Common Values

ValueCountFrequency (%)
RU 30
17.5%
US 16
 
9.4%
CN 11
 
6.4%
GB 10
 
5.8%
AU 8
 
4.7%
KR 8
 
4.7%
ES 5
 
2.9%
CA 5
 
2.9%
NO 3
 
1.8%
JP 2
 
1.2%
Other values (5) 6
 
3.5%
(Missing) 67
39.2%

Length

2023-08-13T15:45:11.426045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ru 30
28.8%
us 16
15.4%
cn 11
 
10.6%
gb 10
 
9.6%
au 8
 
7.7%
kr 8
 
7.7%
es 5
 
4.8%
ca 5
 
4.8%
no 3
 
2.9%
jp 2
 
1.9%
Other values (5) 6
 
5.8%

Most occurring characters

ValueCountFrequency (%)
U 55
26.4%
R 38
18.3%
S 23
11.1%
C 16
 
7.7%
N 15
 
7.2%
A 14
 
6.7%
B 11
 
5.3%
G 10
 
4.8%
E 9
 
4.3%
K 8
 
3.8%
Other values (5) 9
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 208
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 55
26.4%
R 38
18.3%
S 23
11.1%
C 16
 
7.7%
N 15
 
7.2%
A 14
 
6.7%
B 11
 
5.3%
G 10
 
4.8%
E 9
 
4.3%
K 8
 
3.8%
Other values (5) 9
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 208
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 55
26.4%
R 38
18.3%
S 23
11.1%
C 16
 
7.7%
N 15
 
7.2%
A 14
 
6.7%
B 11
 
5.3%
G 10
 
4.8%
E 9
 
4.3%
K 8
 
3.8%
Other values (5) 9
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 208
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 55
26.4%
R 38
18.3%
S 23
11.1%
C 16
 
7.7%
N 15
 
7.2%
A 14
 
6.7%
B 11
 
5.3%
G 10
 
4.8%
E 9
 
4.3%
K 8
 
3.8%
Other values (5) 9
 
4.3%

_embedded.show.webChannel.country.timezone
Categorical

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)14.4%
Missing67
Missing (%)39.2%
Memory size1.5 KiB
Asia/Kamchatka
30 
America/New_York
16 
Asia/Shanghai
11 
Europe/London
10 
Australia/Sydney
8 
Other values (10)
29 

Length

Max length17
Median length16
Mean length13.894231
Min length10

Characters and Unicode

Total characters1445
Distinct characters37
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)3.8%

Sample

1st rowAsia/Kamchatka
2nd rowAsia/Kamchatka
3rd rowAsia/Kamchatka
4th rowAsia/Kamchatka
5th rowAsia/Kamchatka

Common Values

ValueCountFrequency (%)
Asia/Kamchatka 30
17.5%
America/New_York 16
 
9.4%
Asia/Shanghai 11
 
6.4%
Europe/London 10
 
5.8%
Australia/Sydney 8
 
4.7%
Asia/Seoul 8
 
4.7%
Europe/Madrid 5
 
2.9%
America/Halifax 5
 
2.9%
Europe/Oslo 3
 
1.8%
Asia/Tokyo 2
 
1.2%
Other values (5) 6
 
3.5%
(Missing) 67
39.2%

Length

2023-08-13T15:45:11.494916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
asia/kamchatka 30
28.8%
america/new_york 16
15.4%
asia/shanghai 11
 
10.6%
europe/london 10
 
9.6%
australia/sydney 8
 
7.7%
asia/seoul 8
 
7.7%
europe/madrid 5
 
4.8%
america/halifax 5
 
4.8%
europe/oslo 3
 
2.9%
asia/tokyo 2
 
1.9%
Other values (5) 6
 
5.8%

Most occurring characters

ValueCountFrequency (%)
a 217
15.0%
/ 104
 
7.2%
i 102
 
7.1%
A 81
 
5.6%
o 81
 
5.6%
e 81
 
5.6%
r 77
 
5.3%
s 67
 
4.6%
m 55
 
3.8%
h 55
 
3.8%
Other values (27) 525
36.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1101
76.2%
Uppercase Letter 224
 
15.5%
Other Punctuation 104
 
7.2%
Connector Punctuation 16
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 217
19.7%
i 102
 
9.3%
o 81
 
7.4%
e 81
 
7.4%
r 77
 
7.0%
s 67
 
6.1%
m 55
 
5.0%
h 55
 
5.0%
c 53
 
4.8%
k 50
 
4.5%
Other values (12) 263
23.9%
Uppercase Letter
ValueCountFrequency (%)
A 81
36.2%
K 30
 
13.4%
S 29
 
12.9%
E 24
 
10.7%
N 16
 
7.1%
Y 16
 
7.1%
L 10
 
4.5%
M 5
 
2.2%
H 5
 
2.2%
O 3
 
1.3%
Other values (3) 5
 
2.2%
Other Punctuation
ValueCountFrequency (%)
/ 104
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1325
91.7%
Common 120
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 217
16.4%
i 102
 
7.7%
A 81
 
6.1%
o 81
 
6.1%
e 81
 
6.1%
r 77
 
5.8%
s 67
 
5.1%
m 55
 
4.2%
h 55
 
4.2%
c 53
 
4.0%
Other values (25) 456
34.4%
Common
ValueCountFrequency (%)
/ 104
86.7%
_ 16
 
13.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1445
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 217
15.0%
/ 104
 
7.2%
i 102
 
7.1%
A 81
 
5.6%
o 81
 
5.6%
e 81
 
5.6%
r 77
 
5.3%
s 67
 
4.6%
m 55
 
3.8%
h 55
 
3.8%
Other values (27) 525
36.3%

_embedded.show.webChannel.officialSite
Categorical

HIGH CORRELATION  MISSING 

Distinct37
Distinct (%)28.0%
Missing39
Missing (%)22.8%
Memory size1.5 KiB
https://www.youtube.com
17 
https://www.paramountplus.com/
10 
https://www.bbc.co.uk/iplayer
9 
https://binge.com.au/
 
8
https://www.ivi.ru/
 
7
Other values (32)
81 

Length

Max length41
Median length28.5
Mean length23.734848
Min length15

Characters and Unicode

Total characters3133
Distinct characters30
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)9.8%

Sample

1st rowhttps://hd.kinopoisk.ru/
2nd rowhttps://hd.kinopoisk.ru/
3rd rowhttps://www.ivi.ru/
4th rowhttps://www.ivi.ru/
5th rowhttps://www.ivi.ru/

Common Values

ValueCountFrequency (%)
https://www.youtube.com 17
 
9.9%
https://www.paramountplus.com/ 10
 
5.8%
https://www.bbc.co.uk/iplayer 9
 
5.3%
https://binge.com.au/ 8
 
4.7%
https://www.ivi.ru/ 7
 
4.1%
https://www.netflix.com/ 6
 
3.5%
https://v.qq.com/ 6
 
3.5%
https://www.max.com/ 5
 
2.9%
https://hd.kinopoisk.ru/ 5
 
2.9%
https://ver.movistarplus.es/ 5
 
2.9%
Other values (27) 54
31.6%
(Missing) 39
22.8%

Length

2023-08-13T15:45:11.568522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.youtube.com 17
 
12.9%
https://www.paramountplus.com 10
 
7.6%
https://www.bbc.co.uk/iplayer 9
 
6.8%
https://binge.com.au 8
 
6.1%
https://www.ivi.ru 7
 
5.3%
https://www.netflix.com 6
 
4.5%
https://v.qq.com 6
 
4.5%
https://ver.movistarplus.es 5
 
3.8%
https://www.bet.plus 5
 
3.8%
https://www.amazon.com/adlp/freevee-about 5
 
3.8%
Other values (27) 54
40.9%

Most occurring characters

ValueCountFrequency (%)
/ 378
 
12.1%
t 334
 
10.7%
w 273
 
8.7%
. 255
 
8.1%
p 192
 
6.1%
s 180
 
5.7%
o 158
 
5.0%
h 151
 
4.8%
: 132
 
4.2%
u 117
 
3.7%
Other values (20) 963
30.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2362
75.4%
Other Punctuation 765
 
24.4%
Dash Punctuation 5
 
0.2%
Decimal Number 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 334
14.1%
w 273
11.6%
p 192
 
8.1%
s 180
 
7.6%
o 158
 
6.7%
h 151
 
6.4%
u 117
 
5.0%
e 117
 
5.0%
c 115
 
4.9%
m 108
 
4.6%
Other values (15) 617
26.1%
Other Punctuation
ValueCountFrequency (%)
/ 378
49.4%
. 255
33.3%
: 132
 
17.3%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Decimal Number
ValueCountFrequency (%)
4 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2362
75.4%
Common 771
 
24.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 334
14.1%
w 273
11.6%
p 192
 
8.1%
s 180
 
7.6%
o 158
 
6.7%
h 151
 
6.4%
u 117
 
5.0%
e 117
 
5.0%
c 115
 
4.9%
m 108
 
4.6%
Other values (15) 617
26.1%
Common
ValueCountFrequency (%)
/ 378
49.0%
. 255
33.1%
: 132
 
17.1%
- 5
 
0.6%
4 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3133
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 378
 
12.1%
t 334
 
10.7%
w 273
 
8.7%
. 255
 
8.1%
p 192
 
6.1%
s 180
 
5.7%
o 158
 
5.0%
h 151
 
4.8%
: 132
 
4.2%
u 117
 
3.7%
Other values (20) 963
30.7%

_embedded.show.externals.tvrage
Categorical

HIGH CORRELATION  MISSING  UNIFORM 

Distinct2
Distinct (%)100.0%
Missing169
Missing (%)98.8%
Memory size1.5 KiB
1888.0
1 
3171.0
1 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters12
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row1888.0
2nd row3171.0

Common Values

ValueCountFrequency (%)
1888.0 1
 
0.6%
3171.0 1
 
0.6%
(Missing) 169
98.8%

Length

2023-08-13T15:45:11.633120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-13T15:45:11.687295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1888.0 1
50.0%
3171.0 1
50.0%

Most occurring characters

ValueCountFrequency (%)
1 3
25.0%
8 3
25.0%
. 2
16.7%
0 2
16.7%
3 1
 
8.3%
7 1
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10
83.3%
Other Punctuation 2
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3
30.0%
8 3
30.0%
0 2
20.0%
3 1
 
10.0%
7 1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3
25.0%
8 3
25.0%
. 2
16.7%
0 2
16.7%
3 1
 
8.3%
7 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3
25.0%
8 3
25.0%
. 2
16.7%
0 2
16.7%
3 1
 
8.3%
7 1
 
8.3%

_embedded.show.externals.thetvdb
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct76
Distinct (%)64.4%
Missing53
Missing (%)31.0%
Infinite0
Infinite (%)0.0%
Mean390833.08
Minimum75710
Maximum433942
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-08-13T15:45:11.751854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum75710
5-th percentile273918.95
Q1381273
median421119.5
Q3425722
95-th percentile428075
Maximum433942
Range358232
Interquartile range (IQR)44449

Descriptive statistics

Standard deviation65710.561
Coefficient of variation (CV)0.16812948
Kurtosis11.483223
Mean390833.08
Median Absolute Deviation (MAD)6302
Skewness-3.1358906
Sum46118303
Variance4.3178778 × 109
MonotonicityNot monotonic
2023-08-13T15:45:11.831009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
421974 8
 
4.7%
418929 6
 
3.5%
425722 6
 
3.5%
426222 6
 
3.5%
423165 5
 
2.9%
428132 4
 
2.3%
381273 3
 
1.8%
427945 3
 
1.8%
325978 2
 
1.2%
367657 2
 
1.2%
Other values (66) 73
42.7%
(Missing) 53
31.0%
ValueCountFrequency (%)
75710 1
0.6%
78006 1
0.6%
83620 1
0.6%
232731 1
0.6%
248108 1
0.6%
272468 1
0.6%
274175 1
0.6%
290686 1
0.6%
296861 1
0.6%
316876 1
0.6%
ValueCountFrequency (%)
433942 1
 
0.6%
428132 4
2.3%
428075 2
1.2%
427954 1
 
0.6%
427945 3
1.8%
427796 1
 
0.6%
427575 1
 
0.6%
427268 1
 
0.6%
426929 1
 
0.6%
426466 1
 
0.6%

_embedded.show.externals.imdb
Text

MISSING 

Distinct46
Distinct (%)55.4%
Missing88
Missing (%)51.5%
Memory size1.5 KiB
2023-08-13T15:45:11.963499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.7590361
Min length9

Characters and Unicode

Total characters810
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)39.8%

Sample

1st rowtt18228732
2nd rowtt18228732
3rd rowtt18228732
4th rowtt18228732
5th rowtt18228732
ValueCountFrequency (%)
tt18228732 8
 
9.6%
tt19723990 6
 
7.2%
tt11714672 6
 
7.2%
tt13027412 6
 
7.2%
tt20755454 5
 
6.0%
tt23834194 4
 
4.8%
tt11273012 3
 
3.6%
tt11212276 2
 
2.4%
tt8470764 2
 
2.4%
tt14760316 2
 
2.4%
Other values (36) 39
47.0%
2023-08-13T15:45:12.178671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 166
20.5%
2 108
13.3%
1 101
12.5%
4 77
9.5%
7 74
9.1%
0 66
 
8.1%
3 53
 
6.5%
8 49
 
6.0%
9 41
 
5.1%
6 40
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 644
79.5%
Lowercase Letter 166
 
20.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 108
16.8%
1 101
15.7%
4 77
12.0%
7 74
11.5%
0 66
10.2%
3 53
8.2%
8 49
7.6%
9 41
 
6.4%
6 40
 
6.2%
5 35
 
5.4%
Lowercase Letter
ValueCountFrequency (%)
t 166
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 644
79.5%
Latin 166
 
20.5%

Most frequent character per script

Common
ValueCountFrequency (%)
2 108
16.8%
1 101
15.7%
4 77
12.0%
7 74
11.5%
0 66
10.2%
3 53
8.2%
8 49
7.6%
9 41
 
6.4%
6 40
 
6.2%
5 35
 
5.4%
Latin
ValueCountFrequency (%)
t 166
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 810
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 166
20.5%
2 108
13.3%
1 101
12.5%
4 77
9.5%
7 74
9.1%
0 66
 
8.1%
3 53
 
6.5%
8 49
 
6.0%
9 41
 
5.1%
6 40
 
4.9%

_embedded.show.image.medium
Text

MISSING 

Distinct106
Distinct (%)65.0%
Missing8
Missing (%)4.7%
Memory size1.5 KiB
2023-08-13T15:45:12.442261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length72
Median length72
Mean length71.803681
Min length70

Characters and Unicode

Total characters11704
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique80 ?
Unique (%)49.1%

Sample

1st rowhttps://static.tvmaze.com/uploads/images/medium_portrait/324/812265.jpg
2nd rowhttps://static.tvmaze.com/uploads/images/medium_portrait/324/812265.jpg
3rd rowhttps://static.tvmaze.com/uploads/images/medium_portrait/391/978564.jpg
4th rowhttps://static.tvmaze.com/uploads/images/medium_portrait/391/978564.jpg
5th rowhttps://static.tvmaze.com/uploads/images/medium_portrait/425/1064478.jpg
ValueCountFrequency (%)
https://static.tvmaze.com/uploads/images/medium_portrait/431/1077587.jpg 8
 
4.9%
https://static.tvmaze.com/uploads/images/medium_portrait/407/1018744.jpg 7
 
4.3%
https://static.tvmaze.com/uploads/images/medium_portrait/431/1078946.jpg 6
 
3.7%
https://static.tvmaze.com/uploads/images/medium_portrait/432/1081663.jpg 6
 
3.7%
https://static.tvmaze.com/uploads/images/medium_portrait/444/1110324.jpg 6
 
3.7%
https://static.tvmaze.com/uploads/images/medium_portrait/439/1097726.jpg 5
 
3.1%
https://static.tvmaze.com/uploads/images/medium_portrait/436/1090558.jpg 4
 
2.5%
https://static.tvmaze.com/uploads/images/medium_portrait/449/1122658.jpg 3
 
1.8%
https://static.tvmaze.com/uploads/images/medium_portrait/420/1050641.jpg 3
 
1.8%
https://static.tvmaze.com/uploads/images/medium_portrait/432/1080062.jpg 3
 
1.8%
Other values (96) 112
68.7%
2023-08-13T15:45:12.771900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 1141
 
9.7%
/ 1141
 
9.7%
m 815
 
7.0%
a 815
 
7.0%
p 652
 
5.6%
s 652
 
5.6%
i 652
 
5.6%
o 489
 
4.2%
. 489
 
4.2%
e 489
 
4.2%
Other values (22) 4369
37.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8150
69.6%
Other Punctuation 1793
 
15.3%
Decimal Number 1598
 
13.7%
Connector Punctuation 163
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 1141
14.0%
m 815
10.0%
a 815
10.0%
p 652
 
8.0%
s 652
 
8.0%
i 652
 
8.0%
o 489
 
6.0%
e 489
 
6.0%
u 326
 
4.0%
c 326
 
4.0%
Other values (8) 1793
22.0%
Decimal Number
ValueCountFrequency (%)
1 294
18.4%
4 252
15.8%
0 192
12.0%
3 160
10.0%
7 139
8.7%
2 133
8.3%
6 116
 
7.3%
8 112
 
7.0%
9 107
 
6.7%
5 93
 
5.8%
Other Punctuation
ValueCountFrequency (%)
/ 1141
63.6%
. 489
27.3%
: 163
 
9.1%
Connector Punctuation
ValueCountFrequency (%)
_ 163
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8150
69.6%
Common 3554
30.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 1141
14.0%
m 815
10.0%
a 815
10.0%
p 652
 
8.0%
s 652
 
8.0%
i 652
 
8.0%
o 489
 
6.0%
e 489
 
6.0%
u 326
 
4.0%
c 326
 
4.0%
Other values (8) 1793
22.0%
Common
ValueCountFrequency (%)
/ 1141
32.1%
. 489
13.8%
1 294
 
8.3%
4 252
 
7.1%
0 192
 
5.4%
_ 163
 
4.6%
: 163
 
4.6%
3 160
 
4.5%
7 139
 
3.9%
2 133
 
3.7%
Other values (4) 428
 
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11704
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 1141
 
9.7%
/ 1141
 
9.7%
m 815
 
7.0%
a 815
 
7.0%
p 652
 
5.6%
s 652
 
5.6%
i 652
 
5.6%
o 489
 
4.2%
. 489
 
4.2%
e 489
 
4.2%
Other values (22) 4369
37.3%

_embedded.show.image.original
Text

MISSING 

Distinct106
Distinct (%)65.0%
Missing8
Missing (%)4.7%
Memory size1.5 KiB
2023-08-13T15:45:13.041156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length75
Median length75
Mean length74.803681
Min length73

Characters and Unicode

Total characters12193
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique80 ?
Unique (%)49.1%

Sample

1st rowhttps://static.tvmaze.com/uploads/images/original_untouched/324/812265.jpg
2nd rowhttps://static.tvmaze.com/uploads/images/original_untouched/324/812265.jpg
3rd rowhttps://static.tvmaze.com/uploads/images/original_untouched/391/978564.jpg
4th rowhttps://static.tvmaze.com/uploads/images/original_untouched/391/978564.jpg
5th rowhttps://static.tvmaze.com/uploads/images/original_untouched/425/1064478.jpg
ValueCountFrequency (%)
https://static.tvmaze.com/uploads/images/original_untouched/431/1077587.jpg 8
 
4.9%
https://static.tvmaze.com/uploads/images/original_untouched/407/1018744.jpg 7
 
4.3%
https://static.tvmaze.com/uploads/images/original_untouched/431/1078946.jpg 6
 
3.7%
https://static.tvmaze.com/uploads/images/original_untouched/432/1081663.jpg 6
 
3.7%
https://static.tvmaze.com/uploads/images/original_untouched/444/1110324.jpg 6
 
3.7%
https://static.tvmaze.com/uploads/images/original_untouched/439/1097726.jpg 5
 
3.1%
https://static.tvmaze.com/uploads/images/original_untouched/436/1090558.jpg 4
 
2.5%
https://static.tvmaze.com/uploads/images/original_untouched/449/1122658.jpg 3
 
1.8%
https://static.tvmaze.com/uploads/images/original_untouched/420/1050641.jpg 3
 
1.8%
https://static.tvmaze.com/uploads/images/original_untouched/432/1080062.jpg 3
 
1.8%
Other values (96) 112
68.7%
2023-08-13T15:45:13.388331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 1141
 
9.4%
t 978
 
8.0%
a 815
 
6.7%
s 652
 
5.3%
i 652
 
5.3%
o 652
 
5.3%
p 489
 
4.0%
c 489
 
4.0%
. 489
 
4.0%
g 489
 
4.0%
Other values (23) 5347
43.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8639
70.9%
Other Punctuation 1793
 
14.7%
Decimal Number 1598
 
13.1%
Connector Punctuation 163
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 978
 
11.3%
a 815
 
9.4%
s 652
 
7.5%
i 652
 
7.5%
o 652
 
7.5%
p 489
 
5.7%
c 489
 
5.7%
g 489
 
5.7%
m 489
 
5.7%
e 489
 
5.7%
Other values (9) 2445
28.3%
Decimal Number
ValueCountFrequency (%)
1 294
18.4%
4 252
15.8%
0 192
12.0%
3 160
10.0%
7 139
8.7%
2 133
8.3%
6 116
 
7.3%
8 112
 
7.0%
9 107
 
6.7%
5 93
 
5.8%
Other Punctuation
ValueCountFrequency (%)
/ 1141
63.6%
. 489
27.3%
: 163
 
9.1%
Connector Punctuation
ValueCountFrequency (%)
_ 163
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8639
70.9%
Common 3554
29.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 978
 
11.3%
a 815
 
9.4%
s 652
 
7.5%
i 652
 
7.5%
o 652
 
7.5%
p 489
 
5.7%
c 489
 
5.7%
g 489
 
5.7%
m 489
 
5.7%
e 489
 
5.7%
Other values (9) 2445
28.3%
Common
ValueCountFrequency (%)
/ 1141
32.1%
. 489
13.8%
1 294
 
8.3%
4 252
 
7.1%
0 192
 
5.4%
: 163
 
4.6%
_ 163
 
4.6%
3 160
 
4.5%
7 139
 
3.9%
2 133
 
3.7%
Other values (4) 428
 
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12193
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 1141
 
9.4%
t 978
 
8.0%
a 815
 
6.7%
s 652
 
5.3%
i 652
 
5.3%
o 652
 
5.3%
p 489
 
4.0%
c 489
 
4.0%
. 489
 
4.0%
g 489
 
4.0%
Other values (23) 5347
43.9%

_embedded.show.summary
Text

MISSING 

Distinct89
Distinct (%)66.4%
Missing37
Missing (%)21.6%
Memory size1.5 KiB
2023-08-13T15:45:13.592234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length1483
Median length515
Mean length363.01493
Min length43

Characters and Unicode

Total characters48644
Distinct characters99
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70 ?
Unique (%)52.2%

Sample

1st row<p>Alexander Maksimov is a brilliant psychologist and the best negotiator in Russia. He has a huge experience and a large number of rescued people behind his back: he worked during terrorist attacks, persuaded hostages to be released, dissuaded criminals and victims from committing a fatal act. Over the years in the profession, Maximov has developed his own unique method of negotiating. But even a seemingly perfect employee makes mistakes. In the past, Maksimov, negotiating with terrorists who seized the school, is defeated — his daughter is killed. Unable to look into the eyes of his wife and son, Alexander leaves the family. Years later, the hero again encounters the culprit of that tragedy — it turns out that the attacker did not die during the explosion at that school and now throws Maximov a new challenge.</p>
2nd row<p>Centred on Ashley and Gordon, two single(ish), complex humans who are brought together by a car accident and an injured dog, <b>Colin from Accounts</b> is about flawed, funny people choosing each other and being brave enough to show their true self, scars and all, as they navigate life together.</p>
3rd row<p>Centred on Ashley and Gordon, two single(ish), complex humans who are brought together by a car accident and an injured dog, <b>Colin from Accounts</b> is about flawed, funny people choosing each other and being brave enough to show their true self, scars and all, as they navigate life together.</p>
4th row<p>Centred on Ashley and Gordon, two single(ish), complex humans who are brought together by a car accident and an injured dog, <b>Colin from Accounts</b> is about flawed, funny people choosing each other and being brave enough to show their true self, scars and all, as they navigate life together.</p>
5th row<p>Centred on Ashley and Gordon, two single(ish), complex humans who are brought together by a car accident and an injured dog, <b>Colin from Accounts</b> is about flawed, funny people choosing each other and being brave enough to show their true self, scars and all, as they navigate life together.</p>
ValueCountFrequency (%)
the 345
 
4.3%
and 294
 
3.7%
to 216
 
2.7%
a 210
 
2.6%
of 193
 
2.4%
in 146
 
1.8%
is 92
 
1.2%
on 85
 
1.1%
her 77
 
1.0%
their 73
 
0.9%
Other values (2113) 6266
78.4%
2023-08-13T15:45:13.905213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7852
16.1%
e 4682
 
9.6%
t 3031
 
6.2%
a 2976
 
6.1%
n 2852
 
5.9%
o 2773
 
5.7%
i 2669
 
5.5%
r 2467
 
5.1%
s 2439
 
5.0%
h 1957
 
4.0%
Other values (89) 14946
30.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 37240
76.6%
Space Separator 7863
 
16.2%
Other Punctuation 1288
 
2.6%
Uppercase Letter 1282
 
2.6%
Math Symbol 786
 
1.6%
Dash Punctuation 82
 
0.2%
Decimal Number 74
 
0.2%
Close Punctuation 12
 
< 0.1%
Open Punctuation 12
 
< 0.1%
Initial Punctuation 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4682
12.6%
t 3031
 
8.1%
a 2976
 
8.0%
n 2852
 
7.7%
o 2773
 
7.4%
i 2669
 
7.2%
r 2467
 
6.6%
s 2439
 
6.5%
h 1957
 
5.3%
l 1577
 
4.2%
Other values (28) 9817
26.4%
Uppercase Letter
ValueCountFrequency (%)
T 137
 
10.7%
A 109
 
8.5%
C 98
 
7.6%
S 91
 
7.1%
W 63
 
4.9%
L 63
 
4.9%
B 60
 
4.7%
H 60
 
4.7%
J 58
 
4.5%
Y 55
 
4.3%
Other values (16) 488
38.1%
Other Punctuation
ValueCountFrequency (%)
, 502
39.0%
. 391
30.4%
/ 204
15.8%
' 125
 
9.7%
" 19
 
1.5%
? 17
 
1.3%
: 10
 
0.8%
; 9
 
0.7%
! 5
 
0.4%
… 3
 
0.2%
Other values (2) 3
 
0.2%
Decimal Number
ValueCountFrequency (%)
0 16
21.6%
2 15
20.3%
1 12
16.2%
6 7
9.5%
4 7
9.5%
5 5
 
6.8%
9 4
 
5.4%
3 4
 
5.4%
8 2
 
2.7%
7 2
 
2.7%
Dash Punctuation
ValueCountFrequency (%)
- 68
82.9%
– 10
 
12.2%
— 4
 
4.9%
Space Separator
ValueCountFrequency (%)
7852
99.9%
  11
 
0.1%
Math Symbol
ValueCountFrequency (%)
< 393
50.0%
> 393
50.0%
Close Punctuation
ValueCountFrequency (%)
) 11
91.7%
] 1
 
8.3%
Open Punctuation
ValueCountFrequency (%)
( 11
91.7%
[ 1
 
8.3%
Initial Punctuation
ValueCountFrequency (%)
‘ 4
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 38514
79.2%
Common 10122
 
20.8%
Cyrillic 8
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 4682
12.2%
t 3031
 
7.9%
a 2976
 
7.7%
n 2852
 
7.4%
o 2773
 
7.2%
i 2669
 
6.9%
r 2467
 
6.4%
s 2439
 
6.3%
h 1957
 
5.1%
l 1577
 
4.1%
Other values (47) 11091
28.8%
Common
ValueCountFrequency (%)
7852
77.6%
, 502
 
5.0%
< 393
 
3.9%
> 393
 
3.9%
. 391
 
3.9%
/ 204
 
2.0%
' 125
 
1.2%
- 68
 
0.7%
" 19
 
0.2%
? 17
 
0.2%
Other values (25) 158
 
1.6%
Cyrillic
ValueCountFrequency (%)
и 2
25.0%
т 1
12.5%
д 1
12.5%
о 1
12.5%
п 1
12.5%
н 1
12.5%
А 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48582
99.9%
None 33
 
0.1%
Punctuation 21
 
< 0.1%
Cyrillic 8
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7852
16.2%
e 4682
 
9.6%
t 3031
 
6.2%
a 2976
 
6.1%
n 2852
 
5.9%
o 2773
 
5.7%
i 2669
 
5.5%
r 2467
 
5.1%
s 2439
 
5.0%
h 1957
 
4.0%
Other values (71) 14884
30.6%
None
ValueCountFrequency (%)
  11
33.3%
é 10
30.3%
á 5
15.2%
ö 3
 
9.1%
í 2
 
6.1%
å 1
 
3.0%
ä 1
 
3.0%
Punctuation
ValueCountFrequency (%)
– 10
47.6%
— 4
 
19.0%
‘ 4
 
19.0%
… 3
 
14.3%
Cyrillic
ValueCountFrequency (%)
и 2
25.0%
т 1
12.5%
д 1
12.5%
о 1
12.5%
п 1
12.5%
н 1
12.5%
А 1
12.5%

_embedded.show.updated
Real number (ℝ)

HIGH CORRELATION 

Distinct110
Distinct (%)64.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6803126 × 109
Minimum1.6680061 × 109
Maximum1.6918322 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-08-13T15:45:14.004700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.6680061 × 109
5-th percentile1.6707019 × 109
Q11.6727532 × 109
median1.6796917 × 109
Q31.6871324 × 109
95-th percentile1.6912951 × 109
Maximum1.6918322 × 109
Range23826131
Interquartile range (IQR)14379280

Descriptive statistics

Standard deviation7503392.3
Coefficient of variation (CV)0.004465474
Kurtosis-1.5123215
Mean1.6803126 × 109
Median Absolute Deviation (MAD)7231555
Skewness0.12817104
Sum2.8733346 × 1011
Variance5.6300896 × 1013
MonotonicityNot monotonic
2023-08-13T15:45:14.083926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1691295111 8
 
4.7%
1676360040 7
 
4.1%
1687132439 6
 
3.5%
1670701938 6
 
3.5%
1674881954 6
 
3.5%
1672753159 5
 
2.9%
1674373758 5
 
2.9%
1671826084 4
 
2.3%
1670770136 3
 
1.8%
1689534070 3
 
1.8%
Other values (100) 118
69.0%
ValueCountFrequency (%)
1668006079 1
 
0.6%
1668909458 1
 
0.6%
1669792539 2
 
1.2%
1670179582 1
 
0.6%
1670701938 6
3.5%
1670770136 3
1.8%
1671103265 2
 
1.2%
1671145059 1
 
0.6%
1671145408 2
 
1.2%
1671146775 1
 
0.6%
ValueCountFrequency (%)
1691832210 1
 
0.6%
1691759897 1
 
0.6%
1691736532 1
 
0.6%
1691704239 1
 
0.6%
1691694467 1
 
0.6%
1691570800 1
 
0.6%
1691421062 1
 
0.6%
1691295111 8
4.7%
1691256868 1
 
0.6%
1691146974 1
 
0.6%

_embedded.show._links.self.href
Text

Distinct110
Distinct (%)64.3%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-08-13T15:45:14.289074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length34
Mean length33.964912
Min length31

Characters and Unicode

Total characters5808
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83 ?
Unique (%)48.5%

Sample

1st rowhttps://api.tvmaze.com/shows/55724
2nd rowhttps://api.tvmaze.com/shows/55724
3rd rowhttps://api.tvmaze.com/shows/59201
4th rowhttps://api.tvmaze.com/shows/59201
5th rowhttps://api.tvmaze.com/shows/60687
ValueCountFrequency (%)
https://api.tvmaze.com/shows/64860 8
 
4.7%
https://api.tvmaze.com/shows/61880 7
 
4.1%
https://api.tvmaze.com/shows/65188 6
 
3.5%
https://api.tvmaze.com/shows/65365 6
 
3.5%
https://api.tvmaze.com/shows/60736 6
 
3.5%
https://api.tvmaze.com/shows/66150 5
 
2.9%
https://api.tvmaze.com/shows/66583 5
 
2.9%
https://api.tvmaze.com/shows/65911 4
 
2.3%
https://api.tvmaze.com/shows/67168 3
 
1.8%
https://api.tvmaze.com/shows/47633 3
 
1.8%
Other values (100) 118
69.0%
2023-08-13T15:45:14.584243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 684
 
11.8%
s 513
 
8.8%
t 513
 
8.8%
h 342
 
5.9%
p 342
 
5.9%
a 342
 
5.9%
. 342
 
5.9%
o 342
 
5.9%
m 342
 
5.9%
6 202
 
3.5%
Other values (16) 1844
31.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3762
64.8%
Other Punctuation 1197
 
20.6%
Decimal Number 849
 
14.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 513
13.6%
t 513
13.6%
h 342
9.1%
p 342
9.1%
a 342
9.1%
o 342
9.1%
m 342
9.1%
e 171
 
4.5%
w 171
 
4.5%
c 171
 
4.5%
Other values (3) 513
13.6%
Decimal Number
ValueCountFrequency (%)
6 202
23.8%
5 101
11.9%
4 95
11.2%
1 87
10.2%
8 82
9.7%
3 69
 
8.1%
0 62
 
7.3%
2 62
 
7.3%
7 47
 
5.5%
9 42
 
4.9%
Other Punctuation
ValueCountFrequency (%)
/ 684
57.1%
. 342
28.6%
: 171
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 3762
64.8%
Common 2046
35.2%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 684
33.4%
. 342
16.7%
6 202
 
9.9%
: 171
 
8.4%
5 101
 
4.9%
4 95
 
4.6%
1 87
 
4.3%
8 82
 
4.0%
3 69
 
3.4%
0 62
 
3.0%
Other values (3) 151
 
7.4%
Latin
ValueCountFrequency (%)
s 513
13.6%
t 513
13.6%
h 342
9.1%
p 342
9.1%
a 342
9.1%
o 342
9.1%
m 342
9.1%
e 171
 
4.5%
w 171
 
4.5%
c 171
 
4.5%
Other values (3) 513
13.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 684
 
11.8%
s 513
 
8.8%
t 513
 
8.8%
h 342
 
5.9%
p 342
 
5.9%
a 342
 
5.9%
. 342
 
5.9%
o 342
 
5.9%
m 342
 
5.9%
6 202
 
3.5%
Other values (16) 1844
31.7%

_embedded.show._links.previousepisode.href
Text

Distinct110
Distinct (%)64.3%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-08-13T15:45:14.843007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length39
Mean length39
Min length39

Characters and Unicode

Total characters6669
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83 ?
Unique (%)48.5%

Sample

1st rowhttps://api.tvmaze.com/episodes/2575700
2nd rowhttps://api.tvmaze.com/episodes/2575700
3rd rowhttps://api.tvmaze.com/episodes/2438121
4th rowhttps://api.tvmaze.com/episodes/2438121
5th rowhttps://api.tvmaze.com/episodes/2413779
ValueCountFrequency (%)
https://api.tvmaze.com/episodes/2422016 8
 
4.7%
https://api.tvmaze.com/episodes/2433632 7
 
4.1%
https://api.tvmaze.com/episodes/2430806 6
 
3.5%
https://api.tvmaze.com/episodes/2440594 6
 
3.5%
https://api.tvmaze.com/episodes/2433977 6
 
3.5%
https://api.tvmaze.com/episodes/2461025 5
 
2.9%
https://api.tvmaze.com/episodes/2476697 5
 
2.9%
https://api.tvmaze.com/episodes/2452720 4
 
2.3%
https://api.tvmaze.com/episodes/2495539 3
 
1.8%
https://api.tvmaze.com/episodes/2597955 3
 
1.8%
Other values (100) 118
69.0%
2023-08-13T15:45:15.166936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 684
 
10.3%
p 513
 
7.7%
s 513
 
7.7%
e 513
 
7.7%
t 513
 
7.7%
o 342
 
5.1%
a 342
 
5.1%
i 342
 
5.1%
. 342
 
5.1%
m 342
 
5.1%
Other values (16) 2223
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4275
64.1%
Other Punctuation 1197
 
17.9%
Decimal Number 1197
 
17.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p 513
12.0%
s 513
12.0%
e 513
12.0%
t 513
12.0%
o 342
8.0%
a 342
8.0%
i 342
8.0%
m 342
8.0%
h 171
 
4.0%
d 171
 
4.0%
Other values (3) 513
12.0%
Decimal Number
ValueCountFrequency (%)
2 274
22.9%
4 189
15.8%
5 133
11.1%
3 123
10.3%
0 99
 
8.3%
6 82
 
6.9%
9 81
 
6.8%
7 81
 
6.8%
1 72
 
6.0%
8 63
 
5.3%
Other Punctuation
ValueCountFrequency (%)
/ 684
57.1%
. 342
28.6%
: 171
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 4275
64.1%
Common 2394
35.9%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 684
28.6%
. 342
14.3%
2 274
11.4%
4 189
 
7.9%
: 171
 
7.1%
5 133
 
5.6%
3 123
 
5.1%
0 99
 
4.1%
6 82
 
3.4%
9 81
 
3.4%
Other values (3) 216
 
9.0%
Latin
ValueCountFrequency (%)
p 513
12.0%
s 513
12.0%
e 513
12.0%
t 513
12.0%
o 342
8.0%
a 342
8.0%
i 342
8.0%
m 342
8.0%
h 171
 
4.0%
d 171
 
4.0%
Other values (3) 513
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6669
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 684
 
10.3%
p 513
 
7.7%
s 513
 
7.7%
e 513
 
7.7%
t 513
 
7.7%
o 342
 
5.1%
a 342
 
5.1%
i 342
 
5.1%
. 342
 
5.1%
m 342
 
5.1%
Other values (16) 2223
33.3%

image.medium
Text

MISSING 

Distinct80
Distinct (%)100.0%
Missing91
Missing (%)53.2%
Memory size1.5 KiB
2023-08-13T15:45:15.496338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length73
Median length73
Mean length73
Min length73

Characters and Unicode

Total characters5840
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique80 ?
Unique (%)100.0%

Sample

1st rowhttps://static.tvmaze.com/uploads/images/medium_landscape/432/1081693.jpg
2nd rowhttps://static.tvmaze.com/uploads/images/medium_landscape/433/1084040.jpg
3rd rowhttps://static.tvmaze.com/uploads/images/medium_landscape/433/1084095.jpg
4th rowhttps://static.tvmaze.com/uploads/images/medium_landscape/433/1082878.jpg
5th rowhttps://static.tvmaze.com/uploads/images/medium_landscape/433/1084090.jpg
ValueCountFrequency (%)
https://static.tvmaze.com/uploads/images/medium_landscape/432/1081736.jpg 1
 
1.2%
https://static.tvmaze.com/uploads/images/medium_landscape/434/1085583.jpg 1
 
1.2%
https://static.tvmaze.com/uploads/images/medium_landscape/433/1084040.jpg 1
 
1.2%
https://static.tvmaze.com/uploads/images/medium_landscape/433/1084095.jpg 1
 
1.2%
https://static.tvmaze.com/uploads/images/medium_landscape/433/1082878.jpg 1
 
1.2%
https://static.tvmaze.com/uploads/images/medium_landscape/433/1084090.jpg 1
 
1.2%
https://static.tvmaze.com/uploads/images/medium_landscape/433/1084089.jpg 1
 
1.2%
https://static.tvmaze.com/uploads/images/medium_landscape/433/1082867.jpg 1
 
1.2%
https://static.tvmaze.com/uploads/images/medium_landscape/433/1084088.jpg 1
 
1.2%
https://static.tvmaze.com/uploads/images/medium_landscape/433/1082868.jpg 1
 
1.2%
Other values (70) 70
87.5%
2023-08-13T15:45:15.885238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 560
 
9.6%
a 480
 
8.2%
t 400
 
6.8%
s 400
 
6.8%
m 400
 
6.8%
p 320
 
5.5%
e 320
 
5.5%
. 240
 
4.1%
c 240
 
4.1%
d 240
 
4.1%
Other values (22) 2240
38.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4080
69.9%
Other Punctuation 880
 
15.1%
Decimal Number 800
 
13.7%
Connector Punctuation 80
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 480
11.8%
t 400
9.8%
s 400
9.8%
m 400
9.8%
p 320
 
7.8%
e 320
 
7.8%
c 240
 
5.9%
d 240
 
5.9%
i 240
 
5.9%
g 160
 
3.9%
Other values (8) 880
21.6%
Decimal Number
ValueCountFrequency (%)
4 137
17.1%
1 135
16.9%
3 112
14.0%
0 105
13.1%
8 90
11.2%
9 61
7.6%
2 51
 
6.4%
5 44
 
5.5%
7 35
 
4.4%
6 30
 
3.8%
Other Punctuation
ValueCountFrequency (%)
/ 560
63.6%
. 240
27.3%
: 80
 
9.1%
Connector Punctuation
ValueCountFrequency (%)
_ 80
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4080
69.9%
Common 1760
30.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 480
11.8%
t 400
9.8%
s 400
9.8%
m 400
9.8%
p 320
 
7.8%
e 320
 
7.8%
c 240
 
5.9%
d 240
 
5.9%
i 240
 
5.9%
g 160
 
3.9%
Other values (8) 880
21.6%
Common
ValueCountFrequency (%)
/ 560
31.8%
. 240
13.6%
4 137
 
7.8%
1 135
 
7.7%
3 112
 
6.4%
0 105
 
6.0%
8 90
 
5.1%
_ 80
 
4.5%
: 80
 
4.5%
9 61
 
3.5%
Other values (4) 160
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 560
 
9.6%
a 480
 
8.2%
t 400
 
6.8%
s 400
 
6.8%
m 400
 
6.8%
p 320
 
5.5%
e 320
 
5.5%
. 240
 
4.1%
c 240
 
4.1%
d 240
 
4.1%
Other values (22) 2240
38.4%

image.original
Text

MISSING 

Distinct80
Distinct (%)100.0%
Missing91
Missing (%)53.2%
Memory size1.5 KiB
2023-08-13T15:45:16.227084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length75
Median length75
Mean length75
Min length75

Characters and Unicode

Total characters6000
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique80 ?
Unique (%)100.0%

Sample

1st rowhttps://static.tvmaze.com/uploads/images/original_untouched/432/1081693.jpg
2nd rowhttps://static.tvmaze.com/uploads/images/original_untouched/433/1084040.jpg
3rd rowhttps://static.tvmaze.com/uploads/images/original_untouched/433/1084095.jpg
4th rowhttps://static.tvmaze.com/uploads/images/original_untouched/433/1082878.jpg
5th rowhttps://static.tvmaze.com/uploads/images/original_untouched/433/1084090.jpg
ValueCountFrequency (%)
https://static.tvmaze.com/uploads/images/original_untouched/432/1081736.jpg 1
 
1.2%
https://static.tvmaze.com/uploads/images/original_untouched/434/1085583.jpg 1
 
1.2%
https://static.tvmaze.com/uploads/images/original_untouched/433/1084040.jpg 1
 
1.2%
https://static.tvmaze.com/uploads/images/original_untouched/433/1084095.jpg 1
 
1.2%
https://static.tvmaze.com/uploads/images/original_untouched/433/1082878.jpg 1
 
1.2%
https://static.tvmaze.com/uploads/images/original_untouched/433/1084090.jpg 1
 
1.2%
https://static.tvmaze.com/uploads/images/original_untouched/433/1084089.jpg 1
 
1.2%
https://static.tvmaze.com/uploads/images/original_untouched/433/1082867.jpg 1
 
1.2%
https://static.tvmaze.com/uploads/images/original_untouched/433/1084088.jpg 1
 
1.2%
https://static.tvmaze.com/uploads/images/original_untouched/433/1082868.jpg 1
 
1.2%
Other values (70) 70
87.5%
2023-08-13T15:45:16.625960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 560
 
9.3%
t 480
 
8.0%
a 400
 
6.7%
s 320
 
5.3%
i 320
 
5.3%
o 320
 
5.3%
p 240
 
4.0%
c 240
 
4.0%
. 240
 
4.0%
g 240
 
4.0%
Other values (23) 2640
44.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4240
70.7%
Other Punctuation 880
 
14.7%
Decimal Number 800
 
13.3%
Connector Punctuation 80
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 480
 
11.3%
a 400
 
9.4%
s 320
 
7.5%
i 320
 
7.5%
o 320
 
7.5%
p 240
 
5.7%
c 240
 
5.7%
g 240
 
5.7%
m 240
 
5.7%
e 240
 
5.7%
Other values (9) 1200
28.3%
Decimal Number
ValueCountFrequency (%)
4 137
17.1%
1 135
16.9%
3 112
14.0%
0 105
13.1%
8 90
11.2%
9 61
7.6%
2 51
 
6.4%
5 44
 
5.5%
7 35
 
4.4%
6 30
 
3.8%
Other Punctuation
ValueCountFrequency (%)
/ 560
63.6%
. 240
27.3%
: 80
 
9.1%
Connector Punctuation
ValueCountFrequency (%)
_ 80
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4240
70.7%
Common 1760
29.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 480
 
11.3%
a 400
 
9.4%
s 320
 
7.5%
i 320
 
7.5%
o 320
 
7.5%
p 240
 
5.7%
c 240
 
5.7%
g 240
 
5.7%
m 240
 
5.7%
e 240
 
5.7%
Other values (9) 1200
28.3%
Common
ValueCountFrequency (%)
/ 560
31.8%
. 240
13.6%
4 137
 
7.8%
1 135
 
7.7%
3 112
 
6.4%
0 105
 
6.0%
8 90
 
5.1%
: 80
 
4.5%
_ 80
 
4.5%
9 61
 
3.5%
Other values (4) 160
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 560
 
9.3%
t 480
 
8.0%
a 400
 
6.7%
s 320
 
5.3%
i 320
 
5.3%
o 320
 
5.3%
p 240
 
4.0%
c 240
 
4.0%
. 240
 
4.0%
g 240
 
4.0%
Other values (23) 2640
44.0%

_embedded.show.network.id
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)54.5%
Missing149
Missing (%)87.1%
Infinite0
Infinite (%)0.0%
Mean441.09091
Minimum78
Maximum1366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-08-13T15:45:16.707568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum78
5-th percentile80.5
Q1264
median270
Q3514
95-th percentile1355.4
Maximum1366
Range1288
Interquartile range (IQR)250

Descriptive statistics

Standard deviation384.96393
Coefficient of variation (CV)0.87275418
Kurtosis1.6998117
Mean441.09091
Median Absolute Deviation (MAD)140.5
Skewness1.6144441
Sum9704
Variance148197.23
MonotonicityNot monotonic
2023-08-13T15:45:16.767788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
270 8
 
4.7%
514 2
 
1.2%
78 2
 
1.2%
1366 2
 
1.2%
1154 1
 
0.6%
128 1
 
0.6%
262 1
 
0.6%
131 1
 
0.6%
205 1
 
0.6%
739 1
 
0.6%
Other values (2) 2
 
1.2%
(Missing) 149
87.1%
ValueCountFrequency (%)
78 2
 
1.2%
128 1
 
0.6%
131 1
 
0.6%
205 1
 
0.6%
262 1
 
0.6%
270 8
4.7%
451 1
 
0.6%
514 2
 
1.2%
558 1
 
0.6%
739 1
 
0.6%
ValueCountFrequency (%)
1366 2
 
1.2%
1154 1
 
0.6%
739 1
 
0.6%
558 1
 
0.6%
514 2
 
1.2%
451 1
 
0.6%
270 8
4.7%
262 1
 
0.6%
205 1
 
0.6%
131 1
 
0.6%

_embedded.show.network.name
Text

MISSING 

Distinct12
Distinct (%)54.5%
Missing149
Missing (%)87.1%
Memory size1.5 KiB
2023-08-13T15:45:16.857816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length14
Mean length9.7727273
Min length3

Characters and Unicode

Total characters215
Distinct characters40
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)36.4%

Sample

1st rowFox Showcase
2nd rowFox Showcase
3rd rowFox Showcase
4th rowFox Showcase
5th rowFox Showcase
ValueCountFrequency (%)
fox 8
20.5%
showcase 8
20.5%
тв-3 2
 
5.1%
disney 2
 
5.1%
channel 2
 
5.1%
mtv 2
 
5.1%
brasil 2
 
5.1%
nfl 1
 
2.6%
radio-canada 1
 
2.6%
ici 1
 
2.6%
Other values (10) 10
25.6%
2023-08-13T15:45:17.040356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 18
 
8.4%
17
 
7.9%
a 16
 
7.4%
e 13
 
6.0%
s 12
 
5.6%
S 11
 
5.1%
F 10
 
4.7%
h 10
 
4.7%
w 9
 
4.2%
c 8
 
3.7%
Other values (30) 91
42.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 125
58.1%
Uppercase Letter 65
30.2%
Space Separator 17
 
7.9%
Decimal Number 5
 
2.3%
Dash Punctuation 3
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 18
14.4%
a 16
12.8%
e 13
10.4%
s 12
9.6%
h 10
8.0%
w 9
7.2%
c 8
6.4%
x 8
6.4%
n 7
 
5.6%
i 6
 
4.8%
Other values (9) 18
14.4%
Uppercase Letter
ValueCountFrequency (%)
S 11
16.9%
F 10
15.4%
V 6
9.2%
T 6
9.2%
B 5
7.7%
C 5
7.7%
I 3
 
4.6%
M 3
 
4.6%
D 3
 
4.6%
K 3
 
4.6%
Other values (6) 10
15.4%
Decimal Number
ValueCountFrequency (%)
3 3
60.0%
1 1
 
20.0%
2 1
 
20.0%
Space Separator
ValueCountFrequency (%)
17
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 186
86.5%
Common 25
 
11.6%
Cyrillic 4
 
1.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 18
 
9.7%
a 16
 
8.6%
e 13
 
7.0%
s 12
 
6.5%
S 11
 
5.9%
F 10
 
5.4%
h 10
 
5.4%
w 9
 
4.8%
c 8
 
4.3%
x 8
 
4.3%
Other values (23) 71
38.2%
Common
ValueCountFrequency (%)
17
68.0%
3 3
 
12.0%
- 3
 
12.0%
1 1
 
4.0%
2 1
 
4.0%
Cyrillic
ValueCountFrequency (%)
Т 2
50.0%
В 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 209
97.2%
Cyrillic 4
 
1.9%
None 2
 
0.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 18
 
8.6%
17
 
8.1%
a 16
 
7.7%
e 13
 
6.2%
s 12
 
5.7%
S 11
 
5.3%
F 10
 
4.8%
h 10
 
4.8%
w 9
 
4.3%
c 8
 
3.8%
Other values (27) 85
40.7%
None
ValueCountFrequency (%)
é 2
100.0%
Cyrillic
ValueCountFrequency (%)
Т 2
50.0%
В 2
50.0%

_embedded.show.network.country.name
Categorical

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)36.4%
Missing149
Missing (%)87.1%
Memory size1.5 KiB
Australia
9 
United States
4 
Russian Federation
2 
Korea, Republic of
2 
Brazil
2 
Other values (3)
3 

Length

Max length18
Median length13
Mean length10.636364
Min length5

Characters and Unicode

Total characters234
Distinct characters28
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)13.6%

Sample

1st rowAustralia
2nd rowAustralia
3rd rowAustralia
4th rowAustralia
5th rowAustralia

Common Values

ValueCountFrequency (%)
Australia 9
 
5.3%
United States 4
 
2.3%
Russian Federation 2
 
1.2%
Korea, Republic of 2
 
1.2%
Brazil 2
 
1.2%
Japan 1
 
0.6%
Sweden 1
 
0.6%
Canada 1
 
0.6%
(Missing) 149
87.1%

Length

2023-08-13T15:45:17.127380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-13T15:45:17.195697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
australia 9
28.1%
united 4
12.5%
states 4
12.5%
russian 2
 
6.2%
federation 2
 
6.2%
korea 2
 
6.2%
republic 2
 
6.2%
of 2
 
6.2%
brazil 2
 
6.2%
japan 1
 
3.1%
Other values (2) 2
 
6.2%

Most occurring characters

ValueCountFrequency (%)
a 35
15.0%
t 23
 
9.8%
i 21
 
9.0%
e 18
 
7.7%
s 17
 
7.3%
r 15
 
6.4%
u 13
 
5.6%
l 13
 
5.6%
n 11
 
4.7%
10
 
4.3%
Other values (18) 58
24.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 192
82.1%
Uppercase Letter 30
 
12.8%
Space Separator 10
 
4.3%
Other Punctuation 2
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 35
18.2%
t 23
12.0%
i 21
10.9%
e 18
9.4%
s 17
8.9%
r 15
7.8%
u 13
 
6.8%
l 13
 
6.8%
n 11
 
5.7%
d 8
 
4.2%
Other values (7) 18
9.4%
Uppercase Letter
ValueCountFrequency (%)
A 9
30.0%
S 5
16.7%
R 4
13.3%
U 4
13.3%
F 2
 
6.7%
K 2
 
6.7%
B 2
 
6.7%
J 1
 
3.3%
C 1
 
3.3%
Space Separator
ValueCountFrequency (%)
10
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 222
94.9%
Common 12
 
5.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 35
15.8%
t 23
10.4%
i 21
9.5%
e 18
 
8.1%
s 17
 
7.7%
r 15
 
6.8%
u 13
 
5.9%
l 13
 
5.9%
n 11
 
5.0%
A 9
 
4.1%
Other values (16) 47
21.2%
Common
ValueCountFrequency (%)
10
83.3%
, 2
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 234
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 35
15.0%
t 23
 
9.8%
i 21
 
9.0%
e 18
 
7.7%
s 17
 
7.3%
r 15
 
6.4%
u 13
 
5.6%
l 13
 
5.6%
n 11
 
4.7%
10
 
4.3%
Other values (18) 58
24.8%

_embedded.show.network.country.code
Categorical

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)36.4%
Missing149
Missing (%)87.1%
Memory size1.5 KiB
AU
9 
US
4 
RU
2 
KR
2 
BR
2 
Other values (3)
3 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters44
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)13.6%

Sample

1st rowAU
2nd rowAU
3rd rowAU
4th rowAU
5th rowAU

Common Values

ValueCountFrequency (%)
AU 9
 
5.3%
US 4
 
2.3%
RU 2
 
1.2%
KR 2
 
1.2%
BR 2
 
1.2%
JP 1
 
0.6%
SE 1
 
0.6%
CA 1
 
0.6%
(Missing) 149
87.1%

Length

2023-08-13T15:45:17.268807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-13T15:45:17.333485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
au 9
40.9%
us 4
18.2%
ru 2
 
9.1%
kr 2
 
9.1%
br 2
 
9.1%
jp 1
 
4.5%
se 1
 
4.5%
ca 1
 
4.5%

Most occurring characters

ValueCountFrequency (%)
U 15
34.1%
A 10
22.7%
R 6
 
13.6%
S 5
 
11.4%
K 2
 
4.5%
B 2
 
4.5%
J 1
 
2.3%
P 1
 
2.3%
E 1
 
2.3%
C 1
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 44
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 15
34.1%
A 10
22.7%
R 6
 
13.6%
S 5
 
11.4%
K 2
 
4.5%
B 2
 
4.5%
J 1
 
2.3%
P 1
 
2.3%
E 1
 
2.3%
C 1
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 44
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 15
34.1%
A 10
22.7%
R 6
 
13.6%
S 5
 
11.4%
K 2
 
4.5%
B 2
 
4.5%
J 1
 
2.3%
P 1
 
2.3%
E 1
 
2.3%
C 1
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 15
34.1%
A 10
22.7%
R 6
 
13.6%
S 5
 
11.4%
K 2
 
4.5%
B 2
 
4.5%
J 1
 
2.3%
P 1
 
2.3%
E 1
 
2.3%
C 1
 
2.3%

_embedded.show.network.country.timezone
Categorical

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)36.4%
Missing149
Missing (%)87.1%
Memory size1.5 KiB
Australia/Sydney
9 
America/New_York
4 
Asia/Kamchatka
2 
Asia/Seoul
2 
America/Noronha
2 
Other values (3)
3 

Length

Max length16
Median length16
Mean length14.863636
Min length10

Characters and Unicode

Total characters327
Distinct characters30
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)13.6%

Sample

1st rowAustralia/Sydney
2nd rowAustralia/Sydney
3rd rowAustralia/Sydney
4th rowAustralia/Sydney
5th rowAustralia/Sydney

Common Values

ValueCountFrequency (%)
Australia/Sydney 9
 
5.3%
America/New_York 4
 
2.3%
Asia/Kamchatka 2
 
1.2%
Asia/Seoul 2
 
1.2%
America/Noronha 2
 
1.2%
Asia/Tokyo 1
 
0.6%
Europe/Stockholm 1
 
0.6%
America/Halifax 1
 
0.6%
(Missing) 149
87.1%

Length

2023-08-13T15:45:17.410542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-13T15:45:17.481734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
australia/sydney 9
40.9%
america/new_york 4
18.2%
asia/kamchatka 2
 
9.1%
asia/seoul 2
 
9.1%
america/noronha 2
 
9.1%
asia/tokyo 1
 
4.5%
europe/stockholm 1
 
4.5%
america/halifax 1
 
4.5%

Most occurring characters

ValueCountFrequency (%)
a 40
 
12.2%
r 23
 
7.0%
e 23
 
7.0%
i 22
 
6.7%
/ 22
 
6.7%
A 21
 
6.4%
y 19
 
5.8%
o 15
 
4.6%
s 14
 
4.3%
l 13
 
4.0%
Other values (20) 115
35.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 253
77.4%
Uppercase Letter 48
 
14.7%
Other Punctuation 22
 
6.7%
Connector Punctuation 4
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 40
15.8%
r 23
 
9.1%
e 23
 
9.1%
i 22
 
8.7%
y 19
 
7.5%
o 15
 
5.9%
s 14
 
5.5%
l 13
 
5.1%
u 12
 
4.7%
t 12
 
4.7%
Other values (10) 60
23.7%
Uppercase Letter
ValueCountFrequency (%)
A 21
43.8%
S 12
25.0%
N 6
 
12.5%
Y 4
 
8.3%
K 2
 
4.2%
T 1
 
2.1%
E 1
 
2.1%
H 1
 
2.1%
Other Punctuation
ValueCountFrequency (%)
/ 22
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 301
92.0%
Common 26
 
8.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 40
13.3%
r 23
 
7.6%
e 23
 
7.6%
i 22
 
7.3%
A 21
 
7.0%
y 19
 
6.3%
o 15
 
5.0%
s 14
 
4.7%
l 13
 
4.3%
u 12
 
4.0%
Other values (18) 99
32.9%
Common
ValueCountFrequency (%)
/ 22
84.6%
_ 4
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 327
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 40
 
12.2%
r 23
 
7.0%
e 23
 
7.0%
i 22
 
6.7%
/ 22
 
6.7%
A 21
 
6.4%
y 19
 
5.8%
o 15
 
4.6%
s 14
 
4.3%
l 13
 
4.0%
Other values (20) 115
35.2%

_embedded.show.network.officialSite
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)33.3%
Missing159
Missing (%)93.0%
Memory size1.5 KiB
https://www.foxtel.com.au/tv-guide.html/channel/FOX-SHOWCASE/SHC
8 
https://disneynow.com/all-shows/disney-channel
2 
https://www.mavtv.com/
1 
https://ici.radio-canada.ca/tele
1 

Length

Max length64
Median length64
Mean length54.833333
Min length22

Characters and Unicode

Total characters658
Distinct characters34
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)16.7%

Sample

1st rowhttps://www.foxtel.com.au/tv-guide.html/channel/FOX-SHOWCASE/SHC
2nd rowhttps://www.foxtel.com.au/tv-guide.html/channel/FOX-SHOWCASE/SHC
3rd rowhttps://www.foxtel.com.au/tv-guide.html/channel/FOX-SHOWCASE/SHC
4th rowhttps://www.foxtel.com.au/tv-guide.html/channel/FOX-SHOWCASE/SHC
5th rowhttps://www.foxtel.com.au/tv-guide.html/channel/FOX-SHOWCASE/SHC

Common Values

ValueCountFrequency (%)
https://www.foxtel.com.au/tv-guide.html/channel/FOX-SHOWCASE/SHC 8
 
4.7%
https://disneynow.com/all-shows/disney-channel 2
 
1.2%
https://www.mavtv.com/ 1
 
0.6%
https://ici.radio-canada.ca/tele 1
 
0.6%
(Missing) 159
93.0%

Length

2023-08-13T15:45:17.556957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-13T15:45:17.613925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
https://www.foxtel.com.au/tv-guide.html/channel/fox-showcase/shc 8
66.7%
https://disneynow.com/all-shows/disney-channel 2
 
16.7%
https://www.mavtv.com 1
 
8.3%
https://ici.radio-canada.ca/tele 1
 
8.3%

Most occurring characters

ValueCountFrequency (%)
/ 62
 
9.4%
t 50
 
7.6%
. 38
 
5.8%
h 32
 
4.9%
e 32
 
4.9%
w 31
 
4.7%
l 31
 
4.7%
n 27
 
4.1%
a 26
 
4.0%
c 24
 
3.6%
Other values (24) 305
46.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 413
62.8%
Other Punctuation 112
 
17.0%
Uppercase Letter 112
 
17.0%
Dash Punctuation 21
 
3.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 50
12.1%
h 32
 
7.7%
e 32
 
7.7%
w 31
 
7.5%
l 31
 
7.5%
n 27
 
6.5%
a 26
 
6.3%
c 24
 
5.8%
o 24
 
5.8%
m 20
 
4.8%
Other values (11) 116
28.1%
Uppercase Letter
ValueCountFrequency (%)
S 24
21.4%
O 16
14.3%
H 16
14.3%
C 16
14.3%
F 8
 
7.1%
X 8
 
7.1%
W 8
 
7.1%
A 8
 
7.1%
E 8
 
7.1%
Other Punctuation
ValueCountFrequency (%)
/ 62
55.4%
. 38
33.9%
: 12
 
10.7%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 525
79.8%
Common 133
 
20.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 50
 
9.5%
h 32
 
6.1%
e 32
 
6.1%
w 31
 
5.9%
l 31
 
5.9%
n 27
 
5.1%
a 26
 
5.0%
c 24
 
4.6%
S 24
 
4.6%
o 24
 
4.6%
Other values (20) 224
42.7%
Common
ValueCountFrequency (%)
/ 62
46.6%
. 38
28.6%
- 21
 
15.8%
: 12
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 658
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 62
 
9.4%
t 50
 
7.6%
. 38
 
5.8%
h 32
 
4.9%
e 32
 
4.9%
w 31
 
4.7%
l 31
 
4.7%
n 27
 
4.1%
a 26
 
4.0%
c 24
 
3.6%
Other values (24) 305
46.4%

_embedded.show._links.nextepisode.href
Text

MISSING 

Distinct7
Distinct (%)100.0%
Missing164
Missing (%)95.9%
Memory size1.5 KiB
2023-08-13T15:45:17.778475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length39
Mean length39
Min length39

Characters and Unicode

Total characters273
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)100.0%

Sample

1st rowhttps://api.tvmaze.com/episodes/2580326
2nd rowhttps://api.tvmaze.com/episodes/2550406
3rd rowhttps://api.tvmaze.com/episodes/2617844
4th rowhttps://api.tvmaze.com/episodes/2555884
5th rowhttps://api.tvmaze.com/episodes/2545839
ValueCountFrequency (%)
https://api.tvmaze.com/episodes/2580326 1
14.3%
https://api.tvmaze.com/episodes/2550406 1
14.3%
https://api.tvmaze.com/episodes/2617844 1
14.3%
https://api.tvmaze.com/episodes/2555884 1
14.3%
https://api.tvmaze.com/episodes/2545839 1
14.3%
https://api.tvmaze.com/episodes/2572533 1
14.3%
https://api.tvmaze.com/episodes/2493258 1
14.3%
2023-08-13T15:45:18.019793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 28
 
10.3%
p 21
 
7.7%
s 21
 
7.7%
e 21
 
7.7%
t 21
 
7.7%
o 14
 
5.1%
a 14
 
5.1%
i 14
 
5.1%
. 14
 
5.1%
m 14
 
5.1%
Other values (16) 91
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 175
64.1%
Other Punctuation 49
 
17.9%
Decimal Number 49
 
17.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p 21
12.0%
s 21
12.0%
e 21
12.0%
t 21
12.0%
o 14
8.0%
a 14
8.0%
i 14
8.0%
m 14
8.0%
h 7
 
4.0%
d 7
 
4.0%
Other values (3) 21
12.0%
Decimal Number
ValueCountFrequency (%)
5 11
22.4%
2 10
20.4%
8 6
12.2%
4 6
12.2%
3 5
10.2%
0 3
 
6.1%
6 3
 
6.1%
7 2
 
4.1%
9 2
 
4.1%
1 1
 
2.0%
Other Punctuation
ValueCountFrequency (%)
/ 28
57.1%
. 14
28.6%
: 7
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 175
64.1%
Common 98
35.9%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 28
28.6%
. 14
14.3%
5 11
 
11.2%
2 10
 
10.2%
: 7
 
7.1%
8 6
 
6.1%
4 6
 
6.1%
3 5
 
5.1%
0 3
 
3.1%
6 3
 
3.1%
Other values (3) 5
 
5.1%
Latin
ValueCountFrequency (%)
p 21
12.0%
s 21
12.0%
e 21
12.0%
t 21
12.0%
o 14
8.0%
a 14
8.0%
i 14
8.0%
m 14
8.0%
h 7
 
4.0%
d 7
 
4.0%
Other values (3) 21
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 273
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 28
 
10.3%
p 21
 
7.7%
s 21
 
7.7%
e 21
 
7.7%
t 21
 
7.7%
o 14
 
5.1%
a 14
 
5.1%
i 14
 
5.1%
. 14
 
5.1%
m 14
 
5.1%
Other values (16) 91
33.3%

Interactions

2023-08-13T15:45:02.142890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:52.557742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:53.411773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:54.137545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:54.900228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:55.579392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:56.268031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:57.074382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:57.754006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:58.451473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:59.161498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:59.940249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:00.632202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:01.367077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:02.190480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:52.670006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:53.462996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:54.187051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:54.948126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:55.629890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:56.318658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:57.120401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:57.802393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:58.504549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:59.211337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:59.989003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:00.683330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:01.417593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:02.243628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:52.770583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:53.517411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:54.238568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:55.000022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:55.680991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:56.375671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:57.172151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:57.854034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:58.554720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:59.339620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:00.043108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:00.740355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:01.471328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:02.292676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:52.836998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:53.566974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:54.284939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:55.047819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:55.726943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:56.425226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:57.221309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:57.903916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:58.608314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:59.386817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:00.090095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:00.791363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:01.519084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:02.342822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:52.889616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:53.617752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:54.335504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:55.093755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:55.773380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:56.475252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:57.267864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:57.950258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:58.657183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:59.433517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:00.138056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:00.842026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:01.567248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:02.389470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:52.947234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:53.669396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:54.450869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:55.142128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:55.823857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:56.529110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:57.311495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:58.006671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:58.706516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:59.487190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:00.188287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:00.895848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:01.617502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:02.440916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:53.003085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:53.726567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:54.501562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:55.194639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:55.874755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:56.591848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:57.361641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:58.057896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:58.758079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:59.538797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:00.242335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:00.950961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:01.669726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:02.488253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:53.057522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:53.777600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:54.551816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:55.240485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:55.918167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:56.641130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:57.410718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:58.105876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:58.807033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:59.587102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:00.289124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:01.001953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:01.716570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:02.538596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:53.110140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:53.825888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:54.603403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:55.286246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:55.971814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:56.688840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:57.456404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:58.150607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:58.863151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:59.642472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:00.336963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:01.052068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:01.764069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:02.589504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:53.162337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:53.878676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:54.656979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:55.336509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:56.019120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:56.742284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:57.508351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:58.206923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:58.914927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:59.693589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:00.389015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:01.106033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:01.889587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:02.636945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:53.212162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:53.928724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:54.704568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:55.383945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:56.073793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:56.792328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:57.553550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:58.253757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:58.962472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:59.741629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:00.437662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:01.157586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:01.938810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:02.681452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:53.260606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:53.980096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:54.752333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:55.430856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:56.122214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:56.916849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:57.604423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:58.301792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:59.011902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:59.789998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:00.485930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:01.208838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:01.987836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:02.734093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:53.313701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:54.034950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:54.804008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:55.483133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:56.174132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:56.973172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:57.655972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:58.354775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:59.063718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:59.843639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:00.539305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:01.264061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:02.041033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:02.788587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:53.361784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:54.085472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:54.851190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:55.529900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:56.222925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:57.024265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:57.704336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:58.402714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:59.111600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:44:59.891919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:00.587484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:01.315186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-13T15:45:02.088424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-08-13T15:45:18.187164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
idseasonnumberruntimerating.average_embedded.show.id_embedded.show.runtime_embedded.show.averageRuntime_embedded.show.rating.average_embedded.show.weight_embedded.show.webChannel.id_embedded.show.externals.thetvdb_embedded.show.updated_embedded.show.network.idtypeairtimeairstamp_embedded.show.type_embedded.show.language_embedded.show.status_embedded.show.ended_embedded.show.schedule.time_embedded.show.webChannel.country.name_embedded.show.webChannel.country.code_embedded.show.webChannel.country.timezone_embedded.show.webChannel.officialSite_embedded.show.externals.tvrage_embedded.show.network.country.name_embedded.show.network.country.code_embedded.show.network.country.timezone_embedded.show.network.officialSite
id1.0000.0470.114-0.370-0.0630.383-0.447-0.359-0.323-0.518-0.0800.144-0.0580.6020.0000.0000.2210.2500.2870.1770.3310.0000.3320.3320.3320.4981.0000.4230.4230.4230.894
season0.0471.0000.394-0.0000.161-0.7020.1710.012-0.2270.232-0.118-0.8100.3580.2110.0000.3090.2780.4050.0000.1561.0000.0000.0000.0000.0000.3001.0000.0000.0000.0001.000
number0.1140.3941.000-0.3210.142-0.387-0.378-0.325-0.052-0.033-0.267-0.4400.322-0.0631.0000.5120.5270.2680.0000.1770.8730.4840.3420.3420.3420.4481.0000.7390.7390.7390.894
runtime-0.370-0.000-0.3211.000-0.136-0.2170.9720.977-0.0630.173-0.0210.006-0.0070.2010.7000.0000.4090.4550.4770.0000.2200.3980.5610.5610.5610.767NaN0.6190.6190.6190.610
rating.average-0.0630.1610.142-0.1361.000-0.042NaN-0.0360.609-0.3950.301-0.1740.101NaN0.9310.3920.3910.7880.6870.3300.6400.5770.7380.7380.7380.677NaN1.0001.0001.0001.000
_embedded.show.id0.383-0.702-0.387-0.217-0.0421.000-0.365-0.1880.247-0.4980.1550.726-0.2930.1190.4080.4060.3750.2640.0770.1850.5990.3230.2550.2550.2550.4461.0000.7830.7830.7830.943
_embedded.show.runtime-0.4470.171-0.3780.972NaN-0.3651.0000.9900.2930.1820.199-0.3900.0740.4510.6760.0000.4000.4700.4740.0540.6330.3470.5650.5650.5650.7580.0000.5300.5300.5300.707
_embedded.show.averageRuntime-0.3590.012-0.3250.977-0.036-0.1880.9901.000-0.0030.203-0.0350.017-0.0440.2240.6910.0000.3470.4120.5760.1210.2110.3370.6060.6060.6060.6871.0000.5650.5650.5650.894
_embedded.show.rating.average-0.323-0.227-0.052-0.0630.6090.2470.293-0.0031.0000.1580.3610.1680.360-0.6380.0000.3760.4030.3580.0000.3770.5550.4580.3140.3140.3140.527NaN0.0000.0000.0001.000
_embedded.show.weight-0.5180.232-0.0330.173-0.395-0.4980.1820.2030.1581.000-0.132-0.3690.330-0.3300.0930.2640.3760.1970.4030.3460.5340.2370.4580.4580.4580.5331.0000.7140.7140.7141.000
_embedded.show.webChannel.id-0.080-0.118-0.267-0.0210.3010.1550.199-0.0350.361-0.1321.0000.1240.0570.3560.1080.3000.4700.1520.3820.3740.4830.3420.5390.5390.5390.8821.0000.8250.8250.8251.000
_embedded.show.externals.thetvdb0.144-0.810-0.4400.006-0.1740.726-0.3900.0170.168-0.3690.1241.000-0.262-0.3070.2860.5430.5690.3510.2050.1410.7530.4790.3280.3280.3280.5101.0000.7960.7960.7961.000
_embedded.show.updated-0.0580.3580.322-0.0070.101-0.2930.074-0.0440.3600.3300.057-0.2621.0000.1380.0200.2870.3750.1700.3360.4730.4250.2820.4130.4130.4130.6001.0000.6410.6410.6411.000
_embedded.show.network.id0.6020.211-0.0630.201NaN0.1190.4510.224-0.638-0.3300.356-0.3070.1381.0000.4470.6310.7470.7230.5880.7380.5770.8310.8380.8380.8380.8530.0000.6760.6760.6761.000
type0.0000.0001.0000.7000.9310.4080.6760.6910.0000.0930.1080.2860.0200.4471.0000.2460.1490.3820.3890.0001.0000.2450.0000.0000.0000.8551.0000.3870.3870.3871.000
airtime0.0000.3090.5120.0000.3920.4060.0000.0000.3760.2640.3000.5430.2870.6310.2461.0000.8710.2710.2110.4090.7860.8430.4130.4130.4130.7341.0000.6630.6630.6630.736
airstamp0.2210.2780.5270.4090.3910.3750.4000.3470.4030.3760.4700.5690.3750.7470.1490.8711.0000.3110.5090.4510.6700.8240.7840.7840.7840.8011.0000.7790.7790.7790.935
_embedded.show.type0.2500.4050.2680.4550.7880.2640.4700.4120.3580.1970.1520.3510.1700.7230.3820.2710.3111.0000.3290.3540.5130.2690.4130.4130.4130.4871.0000.6730.6730.6731.000
_embedded.show.language0.2870.0000.0000.4770.6870.0770.4740.5760.0000.4030.3820.2050.3360.5880.3890.2110.5090.3291.0000.4100.3950.3670.9590.9590.9590.6441.0000.9660.9660.9660.894
_embedded.show.status0.1770.1560.1770.0000.3300.1850.0540.1210.3770.3460.3740.1410.4730.7380.0000.4090.4510.3540.4101.0001.0000.1760.5510.5510.5510.6691.0000.2640.2640.2640.894
_embedded.show.ended0.3311.0000.8730.2200.6400.5990.6330.2110.5550.5340.4830.7530.4250.5771.0000.7860.6700.5130.3951.0001.0000.8560.6100.6100.6100.6980.0000.8160.8160.8161.000
_embedded.show.schedule.time0.0000.0000.4840.3980.5770.3230.3470.3370.4580.2370.3420.4790.2820.8310.2450.8430.8240.2690.3670.1760.8561.0000.4990.4990.4990.7521.0000.8020.8020.8021.000
_embedded.show.webChannel.country.name0.3320.0000.3420.5610.7380.2550.5650.6060.3140.4580.5390.3280.4130.8380.0000.4130.7840.4130.9590.5510.6100.4991.0001.0001.0000.889NaN0.9260.9260.9261.000
_embedded.show.webChannel.country.code0.3320.0000.3420.5610.7380.2550.5650.6060.3140.4580.5390.3280.4130.8380.0000.4130.7840.4130.9590.5510.6100.4991.0001.0001.0000.889NaN0.9260.9260.9261.000
_embedded.show.webChannel.country.timezone0.3320.0000.3420.5610.7380.2550.5650.6060.3140.4580.5390.3280.4130.8380.0000.4130.7840.4130.9590.5510.6100.4991.0001.0001.0000.889NaN0.9260.9260.9261.000
_embedded.show.webChannel.officialSite0.4980.3000.4480.7670.6770.4460.7580.6870.5270.5330.8820.5100.6000.8530.8550.7340.8010.4870.6440.6690.6980.7520.8890.8890.8891.0001.0000.9530.9530.9530.638
_embedded.show.externals.tvrage1.0001.0001.000NaNNaN1.0000.0001.000NaN1.0001.0001.0001.0000.0001.0001.0001.0001.0001.0001.0000.0001.000NaNNaNNaN1.0001.0000.0000.0000.0000.000
_embedded.show.network.country.name0.4230.0000.7390.6191.0000.7830.5300.5650.0000.7140.8250.7960.6410.6760.3870.6630.7790.6730.9660.2640.8160.8020.9260.9260.9260.9530.0001.0001.0001.0000.943
_embedded.show.network.country.code0.4230.0000.7390.6191.0000.7830.5300.5650.0000.7140.8250.7960.6410.6760.3870.6630.7790.6730.9660.2640.8160.8020.9260.9260.9260.9530.0001.0001.0001.0000.943
_embedded.show.network.country.timezone0.4230.0000.7390.6191.0000.7830.5300.5650.0000.7140.8250.7960.6410.6760.3870.6630.7790.6730.9660.2640.8160.8020.9260.9260.9260.9530.0001.0001.0001.0000.943
_embedded.show.network.officialSite0.8941.0000.8940.6101.0000.9430.7070.8941.0001.0001.0001.0001.0001.0001.0000.7360.9351.0000.8940.8941.0001.0001.0001.0001.0000.6380.0000.9430.9430.9431.000

Missing values

2023-08-13T15:45:02.923236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-13T15:45:03.119506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-08-13T15:45:03.497079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

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1692439198https://www.tvmaze.com/episodes/2439198/de-ferias-com-o-ex-brasil-s09-special-no-estudio-com-o-ex-caribe-s02e06No Estúdio com o Ex Caribe S02E069NaNinsignificant_special2022-12-0122:002022-12-02T00:00:00+00:0060.0NoneNaNhttps://api.tvmaze.com/episodes/2439198https://api.tvmaze.com/shows/3914539145https://www.tvmaze.com/shows/39145/de-ferias-com-o-ex-brasilDe Férias com o Ex BrasilRealityPortuguese[Romance]Running60.060.02018-05-17Nonehttps://www.mtv.com.br/programas/aej2gj/de-ferias-com-o-ex-brasil22:00[Tuesday, Thursday]NaN61NaNNaNNaNNaNNaNNaNNaN348106.0tt8470764https://static.tvmaze.com/uploads/images/medium_portrait/410/1025124.jpghttps://static.tvmaze.com/uploads/images/original_untouched/410/1025124.jpgNone1684343502https://api.tvmaze.com/shows/39145https://api.tvmaze.com/episodes/2550436NaNNaN1366.0MTV BrasilBrazilBRAmerica/NoronhaNoneNaN
1702434906https://www.tvmaze.com/episodes/2434906/puppy-dog-pals-5x34-fixing-santas-sleighFixing Santa's Sleigh534.0regular2022-12-0119:152022-12-02T00:15:00+00:0015.0<p>When Santa's sleigh breaks down mid-delivery, the pets make it their mission to fix it.</p>NaNhttps://api.tvmaze.com/episodes/2434906https://api.tvmaze.com/shows/2634126341https://www.tvmaze.com/shows/26341/puppy-dog-palsPuppy Dog PalsAnimationEnglish[Comedy, Children]Ended15.015.02017-04-142023-01-20https://disneynow.com/shows/puppy-dog-pals08:30[Friday]8.08783.0DisneyNOWUnited StatesUSAmerica/New_Yorkhttps://disneynow.com/NaN325978.0tt6688750https://static.tvmaze.com/uploads/images/medium_portrait/136/342487.jpghttps://static.tvmaze.com/uploads/images/original_untouched/136/342487.jpg<p><b>Puppy Dog Pals </b>is about fun-loving pug puppies, brothers Bingo and Rolly, have thrill-seeking appetites that take them on exhilarating adventures in their neighborhood and around the globe. Whether helping their owner Bob or assisting a friend in need, the pugs' motto is that life is more exciting with your best friend by your side. Each episode features two 11-minute stories that showcase Bingo and Rolly's similarities and differences while demonstrating positive lessons about friendship, problem-solving, collaboration, creativity and adventure.</p>1678525606https://api.tvmaze.com/shows/26341https://api.tvmaze.com/episodes/2452008NaNNaN78.0Disney ChannelUnited StatesUSAmerica/New_Yorkhttps://disneynow.com/all-shows/disney-channelNaN